{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}F:\Mussolini\FPA RnR v2\Appendix\Appendix Log.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}17 Sep 2018, 14:41:50

{com}. do "C:\Users\mtr012\AppData\Local\Temp\STD00000000.tmp"
{txt}
{com}. *Table A3
. 
. logit punish gwf_personalist, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -673.4913}  
Iteration 1:{space 3}log pseudolikelihood = {res:-636.79618}  
Iteration 2:{space 3}log pseudolikelihood = {res:-635.06367}  
Iteration 3:{space 3}log pseudolikelihood = {res:-635.05865}  
Iteration 4:{space 3}log pseudolikelihood = {res:-635.05865}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,188
{txt}{col 49}Wald chi2({res}1{txt}){col 67}= {res}     65.85
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-635.05865{txt}{col 49}Pseudo R2{col 67}= {res}    0.0571

{txt}{ralign 81:(Std. Err. adjusted for {res:134} clusters in ccode)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}         punish{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
gwf_personalist {c |}{col 17}{res}{space 2} 1.705804{col 29}{space 2} .2102053{col 40}{space 1}    8.11{col 49}{space 3}0.000{col 57}{space 4}  1.29381{col 70}{space 3} 2.117799
{txt}{space 10}_cons {c |}{col 17}{res}{space 2}-1.306897{col 29}{space 2} .1374118{col 40}{space 1}   -9.51{col 49}{space 3}0.000{col 57}{space 4}-1.576219{col 70}{space 3}-1.037574
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a1
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}   -673.491{col 42}{txt}Log-Lik Full Model:{col 69}{res}   -635.059
{txt}D(1186):{col 28}{res}   1270.117{col 42}{txt}LR(1):{col 69}{res}     76.865
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.000
{txt}McFadden's R2:{col 28}{res}      0.057{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.054
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.063{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.092
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.078{col 42}{txt}Efron's R2:{col 69}{res}      0.075
{txt}Variance of y*:{col 28}{res}      3.568{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.767{col 42}{txt}Adj Count R2:{col 69}{res}      0.083
{txt}AIC:{col 28}{res}      1.072{col 42}{txt}AIC*n:{col 69}{res}   1274.117
{txt}BIC:{col 28}{res}  -7126.794{col 42}{txt}BIC':{col 69}{res}    -69.785
{txt}BIC used by Stata:{col 28}{res}   1284.277{col 42}{txt}AIC used by Stata:{col 69}{res}   1274.117
{txt}
{com}. 
. logit punish gwf_personalist irr_entry  max_both_max_pts previous_sum_punish instit_control, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-266.00598}  
Iteration 1:{space 3}log pseudolikelihood = {res:-204.36804}  
Iteration 2:{space 3}log pseudolikelihood = {res:-197.96034}  
Iteration 3:{space 3}log pseudolikelihood = {res:-197.65011}  
Iteration 4:{space 3}log pseudolikelihood = {res:-197.64918}  
Iteration 5:{space 3}log pseudolikelihood = {res:-197.64918}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       540
{txt}{col 49}Wald chi2({res}5{txt}){col 67}= {res}     88.97
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-197.64918{txt}{col 49}Pseudo R2{col 67}= {res}    0.2570

{txt}{ralign 85:(Std. Err. adjusted for {res:117} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}             punish{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}gwf_personalist {c |}{col 21}{res}{space 2} 1.539387{col 33}{space 2} .4867393{col 44}{space 1}    3.16{col 53}{space 3}0.002{col 61}{space 4}  .585395{col 74}{space 3} 2.493378
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2} .6325488{col 33}{space 2} .3538778{col 44}{space 1}    1.79{col 53}{space 3}0.074{col 61}{space 4}-.0610389{col 74}{space 3} 1.326136
{txt}{space 3}max_both_max_pts {c |}{col 21}{res}{space 2} .6335194{col 33}{space 2} .1186657{col 44}{space 1}    5.34{col 53}{space 3}0.000{col 61}{space 4} .4009389{col 74}{space 3}    .8661
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0235768{col 33}{space 2}  .012992{col 44}{space 1}    1.81{col 53}{space 3}0.070{col 61}{space 4} -.001887{col 74}{space 3} .0490405
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-1.542691{col 33}{space 2} .4123061{col 44}{space 1}   -3.74{col 53}{space 3}0.000{col 61}{space 4}-2.350796{col 74}{space 3}-.7345855
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-2.700391{col 33}{space 2}  .541465{col 44}{space 1}   -4.99{col 53}{space 3}0.000{col 61}{space 4}-3.761643{col 74}{space 3}-1.639139
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a2
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}   -266.006{col 42}{txt}Log-Lik Full Model:{col 69}{res}   -197.649
{txt}D(534):{col 28}{res}    395.298{col 42}{txt}LR(5):{col 69}{res}    136.714
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.000
{txt}McFadden's R2:{col 28}{res}      0.257{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.234
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.224{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.357
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.371{col 42}{txt}Efron's R2:{col 69}{res}      0.260
{txt}Variance of y*:{col 28}{res}      5.229{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.843{col 42}{txt}Adj Count R2:{col 69}{res}      0.190
{txt}AIC:{col 28}{res}      0.754{col 42}{txt}AIC*n:{col 69}{res}    407.298
{txt}BIC:{col 28}{res}  -2964.400{col 42}{txt}BIC':{col 69}{res}   -105.256
{txt}BIC used by Stata:{col 28}{res}    433.048{col 42}{txt}AIC used by Stata:{col 69}{res}    407.298
{txt}
{com}. 
. logit punish gwf_personalist  irr_entry max_both_max_pts previous_sum_punish instit_control if gwf_democracy==0, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-101.14469}  
Iteration 1:{space 3}log pseudolikelihood = {res:-90.752819}  
Iteration 2:{space 3}log pseudolikelihood = {res:-90.746698}  
Iteration 3:{space 3}log pseudolikelihood = {res:-90.746698}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       146
{txt}{col 49}Wald chi2({res}5{txt}){col 67}= {res}     18.16
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0027
{txt}Log pseudolikelihood = {res}-90.746698{txt}{col 49}Pseudo R2{col 67}= {res}    0.1028

{txt}{ralign 85:(Std. Err. adjusted for {res:76} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}             punish{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}gwf_personalist {c |}{col 21}{res}{space 2} 1.279286{col 33}{space 2} .4621627{col 44}{space 1}    2.77{col 53}{space 3}0.006{col 61}{space 4} .3734635{col 74}{space 3} 2.185108
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2} .5346683{col 33}{space 2} .4172454{col 44}{space 1}    1.28{col 53}{space 3}0.200{col 61}{space 4}-.2831175{col 74}{space 3} 1.352454
{txt}{space 3}max_both_max_pts {c |}{col 21}{res}{space 2} .2030239{col 33}{space 2} .2195349{col 44}{space 1}    0.92{col 53}{space 3}0.355{col 61}{space 4}-.2272566{col 74}{space 3} .6333045
{txt}previous_sum_punish {c |}{col 21}{res}{space 2}-.0110551{col 33}{space 2} .0225058{col 44}{space 1}   -0.49{col 53}{space 3}0.623{col 61}{space 4}-.0551657{col 74}{space 3} .0330555
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.6982173{col 33}{space 2} .4119946{col 44}{space 1}   -1.69{col 53}{space 3}0.090{col 61}{space 4}-1.505712{col 74}{space 3} .1092773
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-.9233899{col 33}{space 2} .8233244{col 44}{space 1}   -1.12{col 53}{space 3}0.262{col 61}{space 4}-2.537076{col 74}{space 3} .6902963
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a3
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}   -101.145{col 42}{txt}Log-Lik Full Model:{col 69}{res}    -90.747
{txt}D(140):{col 28}{res}    181.493{col 42}{txt}LR(5):{col 69}{res}     20.796
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.001
{txt}McFadden's R2:{col 28}{res}      0.103{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.043
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.133{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.177
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.173{col 42}{txt}Efron's R2:{col 69}{res}      0.132
{txt}Variance of y*:{col 28}{res}      3.978{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.637{col 42}{txt}Adj Count R2:{col 69}{res}      0.254
{txt}AIC:{col 28}{res}      1.325{col 42}{txt}AIC*n:{col 69}{res}    193.493
{txt}BIC:{col 28}{res}   -516.212{col 42}{txt}BIC':{col 69}{res}      4.122
{txt}BIC used by Stata:{col 28}{res}    211.395{col 42}{txt}AIC used by Stata:{col 69}{res}    193.493
{txt}
{com}. 
{txt}end of do-file

{com}. do "C:\Users\mtr012\AppData\Local\Temp\STD00000000.tmp"
{txt}
{com}. *Table A4
. 
. logit punish max_person_scale, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-542.97537}  
Iteration 1:{space 3}log pseudolikelihood = {res:-446.87382}  
Iteration 2:{space 3}log pseudolikelihood = {res: -440.4967}  
Iteration 3:{space 3}log pseudolikelihood = {res:-440.35601}  
Iteration 4:{space 3}log pseudolikelihood = {res:-440.35598}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,024
{txt}{col 49}Wald chi2({res}1{txt}){col 67}= {res}     94.99
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-440.35598{txt}{col 49}Pseudo R2{col 67}= {res}    0.1890

{txt}{ralign 82:(Std. Err. adjusted for {res:131} clusters in ccode)}
{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}          punish{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
max_person_scale {c |}{col 18}{res}{space 2} 2.952284{col 30}{space 2}  .302914{col 41}{space 1}    9.75{col 50}{space 3}0.000{col 58}{space 4} 2.358584{col 71}{space 3} 3.545985
{txt}{space 11}_cons {c |}{col 18}{res}{space 2}-1.940859{col 30}{space 2} .1788635{col 41}{space 1}  -10.85{col 50}{space 3}0.000{col 58}{space 4}-2.291425{col 71}{space 3}-1.590293
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a4
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}   -542.975{col 42}{txt}Log-Lik Full Model:{col 69}{res}   -440.356
{txt}D(1022):{col 28}{res}    880.712{col 42}{txt}LR(1):{col 69}{res}    205.239
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.000
{txt}McFadden's R2:{col 28}{res}      0.189{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.185
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.182{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.278
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.231{col 42}{txt}Efron's R2:{col 69}{res}      0.227
{txt}Variance of y*:{col 28}{res}      4.277{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.818{col 42}{txt}Adj Count R2:{col 69}{res}      0.184
{txt}AIC:{col 28}{res}      0.864{col 42}{txt}AIC*n:{col 69}{res}    884.712
{txt}BIC:{col 28}{res}  -6203.252{col 42}{txt}BIC':{col 69}{res}   -198.307
{txt}BIC used by Stata:{col 28}{res}    894.575{col 42}{txt}AIC used by Stata:{col 69}{res}    884.712
{txt}
{com}. 
. logit punish max_person_scale gwf_democracy irr_entry  max_both_max_pts previous_sum_punish instit_control, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-222.87153}  
Iteration 1:{space 3}log pseudolikelihood = {res:-165.80252}  
Iteration 2:{space 3}log pseudolikelihood = {res:-157.90219}  
Iteration 3:{space 3}log pseudolikelihood = {res:-157.51053}  
Iteration 4:{space 3}log pseudolikelihood = {res:-157.50923}  
Iteration 5:{space 3}log pseudolikelihood = {res:-157.50923}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       459
{txt}{col 49}Wald chi2({res}6{txt}){col 67}= {res}     79.81
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-157.50923{txt}{col 49}Pseudo R2{col 67}= {res}    0.2933

{txt}{ralign 85:(Std. Err. adjusted for {res:109} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}             punish{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}max_person_scale {c |}{col 21}{res}{space 2}  1.70656{col 33}{space 2} .5058502{col 44}{space 1}    3.37{col 53}{space 3}0.001{col 61}{space 4} .7151118{col 74}{space 3} 2.698008
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2}-1.039071{col 33}{space 2} .3244186{col 44}{space 1}   -3.20{col 53}{space 3}0.001{col 61}{space 4} -1.67492{col 74}{space 3}-.4032222
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2} -.334691{col 33}{space 2} .5012504{col 44}{space 1}   -0.67{col 53}{space 3}0.504{col 61}{space 4}-1.317124{col 74}{space 3} .6477417
{txt}{space 3}max_both_max_pts {c |}{col 21}{res}{space 2} .6023805{col 33}{space 2} .1407957{col 44}{space 1}    4.28{col 53}{space 3}0.000{col 61}{space 4}  .326426{col 74}{space 3} .8783349
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0104899{col 33}{space 2} .0150305{col 44}{space 1}    0.70{col 53}{space 3}0.485{col 61}{space 4}-.0189694{col 74}{space 3} .0399492
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-1.104235{col 33}{space 2} .4641785{col 44}{space 1}   -2.38{col 53}{space 3}0.017{col 61}{space 4}-2.014008{col 74}{space 3}-.1944618
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-2.252637{col 33}{space 2} .6841533{col 44}{space 1}   -3.29{col 53}{space 3}0.001{col 61}{space 4}-3.593553{col 74}{space 3}-.9117216
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a5
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}   -222.872{col 42}{txt}Log-Lik Full Model:{col 69}{res}   -157.509
{txt}D(452):{col 28}{res}    315.018{col 42}{txt}LR(6):{col 69}{res}    130.725
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.000
{txt}McFadden's R2:{col 28}{res}      0.293{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.262
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.248{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.399
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.404{col 42}{txt}Efron's R2:{col 69}{res}      0.298
{txt}Variance of y*:{col 28}{res}      5.522{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.839{col 42}{txt}Adj Count R2:{col 69}{res}      0.149
{txt}AIC:{col 28}{res}      0.717{col 42}{txt}AIC*n:{col 69}{res}    329.018
{txt}BIC:{col 28}{res}  -2455.312{col 42}{txt}BIC':{col 69}{res}    -93.950
{txt}BIC used by Stata:{col 28}{res}    357.922{col 42}{txt}AIC used by Stata:{col 69}{res}    329.018
{txt}
{com}. 
. logit punish max_person_scale  irr_entry max_both_max_pts previous_sum_punish instit_control if gwf_democracy==0, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-83.110983}  
Iteration 1:{space 3}log pseudolikelihood = {res:-72.416642}  
Iteration 2:{space 3}log pseudolikelihood = {res:-72.395791}  
Iteration 3:{space 3}log pseudolikelihood = {res:-72.395788}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       120
{txt}{col 49}Wald chi2({res}5{txt}){col 67}= {res}     16.49
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0056
{txt}Log pseudolikelihood = {res}-72.395788{txt}{col 49}Pseudo R2{col 67}= {res}    0.1289

{txt}{ralign 85:(Std. Err. adjusted for {res:71} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}             punish{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}max_person_scale {c |}{col 21}{res}{space 2} 1.575526{col 33}{space 2} .5902285{col 44}{space 1}    2.67{col 53}{space 3}0.008{col 61}{space 4} .4186994{col 74}{space 3} 2.732353
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2} .0366628{col 33}{space 2} .5264685{col 44}{space 1}    0.07{col 53}{space 3}0.944{col 61}{space 4}-.9951965{col 74}{space 3} 1.068522
{txt}{space 3}max_both_max_pts {c |}{col 21}{res}{space 2} .3202751{col 33}{space 2} .2935094{col 44}{space 1}    1.09{col 53}{space 3}0.275{col 61}{space 4}-.2549928{col 74}{space 3}  .895543
{txt}previous_sum_punish {c |}{col 21}{res}{space 2}-.0308859{col 33}{space 2} .0233034{col 44}{space 1}   -1.33{col 53}{space 3}0.185{col 61}{space 4}-.0765597{col 74}{space 3} .0147879
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.7110652{col 33}{space 2} .5124254{col 44}{space 1}   -1.39{col 53}{space 3}0.165{col 61}{space 4}-1.715401{col 74}{space 3} .2932702
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-1.339948{col 33}{space 2} .9963728{col 44}{space 1}   -1.34{col 53}{space 3}0.179{col 61}{space 4}-3.292803{col 74}{space 3} .6129067
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a6
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}    -83.111{col 42}{txt}Log-Lik Full Model:{col 69}{res}    -72.396
{txt}D(114):{col 28}{res}    144.792{col 42}{txt}LR(5):{col 69}{res}     21.430
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.001
{txt}McFadden's R2:{col 28}{res}      0.129{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.057
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.164{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.218
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.212{col 42}{txt}Efron's R2:{col 69}{res}      0.166
{txt}Variance of y*:{col 28}{res}      4.174{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.675{col 42}{txt}Adj Count R2:{col 69}{res}      0.328
{txt}AIC:{col 28}{res}      1.307{col 42}{txt}AIC*n:{col 69}{res}    156.792
{txt}BIC:{col 28}{res}   -400.982{col 42}{txt}BIC':{col 69}{res}      2.507
{txt}BIC used by Stata:{col 28}{res}    173.517{col 42}{txt}AIC used by Stata:{col 69}{res}    156.792
{txt}
{com}. 
{txt}end of do-file

{com}. do "C:\Users\mtr012\AppData\Local\Temp\STD00000000.tmp"
{txt}
{com}. *Table A5
. 
. logit punish avg_person_scale, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-542.97537}  
Iteration 1:{space 3}log pseudolikelihood = {res:-463.32409}  
Iteration 2:{space 3}log pseudolikelihood = {res:-459.67074}  
Iteration 3:{space 3}log pseudolikelihood = {res:-459.57722}  
Iteration 4:{space 3}log pseudolikelihood = {res:-459.57718}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,024
{txt}{col 49}Wald chi2({res}1{txt}){col 67}= {res}     59.37
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-459.57718{txt}{col 49}Pseudo R2{col 67}= {res}    0.1536

{txt}{ralign 82:(Std. Err. adjusted for {res:131} clusters in ccode)}
{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}          punish{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
avg_person_scale {c |}{col 18}{res}{space 2} 3.349718{col 30}{space 2} .4347478{col 41}{space 1}    7.70{col 50}{space 3}0.000{col 58}{space 4} 2.497628{col 71}{space 3} 4.201808
{txt}{space 11}_cons {c |}{col 18}{res}{space 2}-1.781998{col 30}{space 2} .1664556{col 41}{space 1}  -10.71{col 50}{space 3}0.000{col 58}{space 4}-2.108245{col 71}{space 3}-1.455751
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a7
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}   -542.975{col 42}{txt}Log-Lik Full Model:{col 69}{res}   -459.577
{txt}D(1022):{col 28}{res}    919.154{col 42}{txt}LR(1):{col 69}{res}    166.796
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.000
{txt}McFadden's R2:{col 28}{res}      0.154{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.150
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.150{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.230
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.197{col 42}{txt}Efron's R2:{col 69}{res}      0.184
{txt}Variance of y*:{col 28}{res}      4.097{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.809{col 42}{txt}Adj Count R2:{col 69}{res}      0.140
{txt}AIC:{col 28}{res}      0.902{col 42}{txt}AIC*n:{col 69}{res}    923.154
{txt}BIC:{col 28}{res}  -6164.810{col 42}{txt}BIC':{col 69}{res}   -159.865
{txt}BIC used by Stata:{col 28}{res}    933.017{col 42}{txt}AIC used by Stata:{col 69}{res}    923.154
{txt}
{com}. 
. logit punish avg_person_scale  gwf_democracy irr_entry  max_both_max_pts previous_sum_punish instit_control, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-222.87153}  
Iteration 1:{space 3}log pseudolikelihood = {res:-163.70366}  
Iteration 2:{space 3}log pseudolikelihood = {res:-157.09345}  
Iteration 3:{space 3}log pseudolikelihood = {res:-156.57262}  
Iteration 4:{space 3}log pseudolikelihood = {res: -156.5719}  
Iteration 5:{space 3}log pseudolikelihood = {res: -156.5719}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       459
{txt}{col 49}Wald chi2({res}6{txt}){col 67}= {res}     68.25
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res} -156.5719{txt}{col 49}Pseudo R2{col 67}= {res}    0.2975

{txt}{ralign 85:(Std. Err. adjusted for {res:109} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}             punish{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}avg_person_scale {c |}{col 21}{res}{space 2} 2.406963{col 33}{space 2} .6175326{col 44}{space 1}    3.90{col 53}{space 3}0.000{col 61}{space 4} 1.196621{col 74}{space 3} 3.617304
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2}-.9649011{col 33}{space 2}  .323055{col 44}{space 1}   -2.99{col 53}{space 3}0.003{col 61}{space 4}-1.598077{col 74}{space 3} -.331725
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2}-.3726537{col 33}{space 2} .4560401{col 44}{space 1}   -0.82{col 53}{space 3}0.414{col 61}{space 4}-1.266476{col 74}{space 3} .5211684
{txt}{space 3}max_both_max_pts {c |}{col 21}{res}{space 2} .6332296{col 33}{space 2}  .139278{col 44}{space 1}    4.55{col 53}{space 3}0.000{col 61}{space 4} .3602497{col 74}{space 3} .9062095
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0153359{col 33}{space 2} .0174418{col 44}{space 1}    0.88{col 53}{space 3}0.379{col 61}{space 4}-.0188494{col 74}{space 3} .0495212
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-1.068806{col 33}{space 2} .4878925{col 44}{space 1}   -2.19{col 53}{space 3}0.028{col 61}{space 4}-2.025058{col 74}{space 3}-.1125542
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-2.439622{col 33}{space 2} .7222921{col 44}{space 1}   -3.38{col 53}{space 3}0.001{col 61}{space 4}-3.855288{col 74}{space 3}-1.023955
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a8
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}   -222.872{col 42}{txt}Log-Lik Full Model:{col 69}{res}   -156.572
{txt}D(452):{col 28}{res}    313.144{col 42}{txt}LR(6):{col 69}{res}    132.599
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.000
{txt}McFadden's R2:{col 28}{res}      0.297{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.266
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.251{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.404
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.416{col 42}{txt}Efron's R2:{col 69}{res}      0.302
{txt}Variance of y*:{col 28}{res}      5.633{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.850{col 42}{txt}Adj Count R2:{col 69}{res}      0.207
{txt}AIC:{col 28}{res}      0.713{col 42}{txt}AIC*n:{col 69}{res}    327.144
{txt}BIC:{col 28}{res}  -2457.187{col 42}{txt}BIC':{col 69}{res}    -95.825
{txt}BIC used by Stata:{col 28}{res}    356.047{col 42}{txt}AIC used by Stata:{col 69}{res}    327.144
{txt}
{com}. 
. logit punish avg_person_scale  irr_entry max_both_max_pts previous_sum_punish instit_control if gwf_democracy==0, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-83.110983}  
Iteration 1:{space 3}log pseudolikelihood = {res:-70.400865}  
Iteration 2:{space 3}log pseudolikelihood = {res:-70.345248}  
Iteration 3:{space 3}log pseudolikelihood = {res:-70.345184}  
Iteration 4:{space 3}log pseudolikelihood = {res:-70.345184}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       120
{txt}{col 49}Wald chi2({res}5{txt}){col 67}= {res}     19.03
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0019
{txt}Log pseudolikelihood = {res}-70.345184{txt}{col 49}Pseudo R2{col 67}= {res}    0.1536

{txt}{ralign 85:(Std. Err. adjusted for {res:71} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}             punish{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}avg_person_scale {c |}{col 21}{res}{space 2} 2.243459{col 33}{space 2}  .678752{col 44}{space 1}    3.31{col 53}{space 3}0.001{col 61}{space 4} .9131294{col 74}{space 3} 3.573788
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2}-.0859345{col 33}{space 2} .5005448{col 44}{space 1}   -0.17{col 53}{space 3}0.864{col 61}{space 4}-1.066984{col 74}{space 3} .8951153
{txt}{space 3}max_both_max_pts {c |}{col 21}{res}{space 2} .3438912{col 33}{space 2}  .292605{col 44}{space 1}    1.18{col 53}{space 3}0.240{col 61}{space 4} -.229604{col 74}{space 3} .9173864
{txt}previous_sum_punish {c |}{col 21}{res}{space 2}-.0146769{col 33}{space 2} .0302939{col 44}{space 1}   -0.48{col 53}{space 3}0.628{col 61}{space 4}-.0740519{col 74}{space 3}  .044698
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.7365332{col 33}{space 2} .5243612{col 44}{space 1}   -1.40{col 53}{space 3}0.160{col 61}{space 4}-1.764262{col 74}{space 3} .2911958
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-1.484922{col 33}{space 2} 1.035709{col 44}{space 1}   -1.43{col 53}{space 3}0.152{col 61}{space 4}-3.514875{col 74}{space 3} .5450306
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a9
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}    -83.111{col 42}{txt}Log-Lik Full Model:{col 69}{res}    -70.345
{txt}D(114):{col 28}{res}    140.690{col 42}{txt}LR(5):{col 69}{res}     25.532
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.000
{txt}McFadden's R2:{col 28}{res}      0.154{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.081
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.192{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.256
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.255{col 42}{txt}Efron's R2:{col 69}{res}      0.189
{txt}Variance of y*:{col 28}{res}      4.415{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.675{col 42}{txt}Adj Count R2:{col 69}{res}      0.328
{txt}AIC:{col 28}{res}      1.272{col 42}{txt}AIC*n:{col 69}{res}    152.690
{txt}BIC:{col 28}{res}   -405.084{col 42}{txt}BIC':{col 69}{res}     -1.594
{txt}BIC used by Stata:{col 28}{res}    169.415{col 42}{txt}AIC used by Stata:{col 69}{res}    152.690
{txt}
{com}. 
{txt}end of do-file

{com}. do "C:\Users\mtr012\AppData\Local\Temp\STD00000000.tmp"
{txt}
{com}. *Table A6
. 
. logit punish max_pers_magaloni, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-721.22415}  
Iteration 1:{space 3}log pseudolikelihood = {res:-566.26853}  
Iteration 2:{space 3}log pseudolikelihood = {res:-557.92359}  
Iteration 3:{space 3}log pseudolikelihood = {res:-557.86837}  
Iteration 4:{space 3}log pseudolikelihood = {res:-557.86836}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,285
{txt}{col 49}Wald chi2({res}1{txt}){col 67}= {res}    124.82
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-557.86836{txt}{col 49}Pseudo R2{col 67}= {res}    0.2265

{txt}{ralign 83:(Std. Err. adjusted for {res:153} clusters in ccode)}
{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}           punish{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
max_pers_magaloni {c |}{col 19}{res}{space 2} 1.489342{col 31}{space 2} .1333073{col 42}{space 1}   11.17{col 51}{space 3}0.000{col 59}{space 4} 1.228064{col 72}{space 3} 1.750619
{txt}{space 12}_cons {c |}{col 19}{res}{space 2} -2.33358{col 31}{space 2} .1751095{col 42}{space 1}  -13.33{col 51}{space 3}0.000{col 59}{space 4}-2.676788{col 72}{space 3}-1.990371
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a10
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}   -721.224{col 42}{txt}Log-Lik Full Model:{col 69}{res}   -557.868
{txt}D(1283):{col 28}{res}   1115.737{col 42}{txt}LR(1):{col 69}{res}    326.712
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.000
{txt}McFadden's R2:{col 28}{res}      0.226{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.224
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.225{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.333
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.304{col 42}{txt}Efron's R2:{col 69}{res}      0.254
{txt}Variance of y*:{col 28}{res}      4.730{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.798{col 42}{txt}Adj Count R2:{col 69}{res}      0.191
{txt}AIC:{col 28}{res}      0.871{col 42}{txt}AIC*n:{col 69}{res}   1119.737
{txt}BIC:{col 28}{res}  -8068.637{col 42}{txt}BIC':{col 69}{res}   -319.553
{txt}BIC used by Stata:{col 28}{res}   1130.054{col 42}{txt}AIC used by Stata:{col 69}{res}   1119.737
{txt}
{com}. 
. logit punish max_pers_magaloni gwf_democracy irr_entry  max_both_max_pts previous_sum_punish instit_control, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-264.48135}  
Iteration 1:{space 3}log pseudolikelihood = {res:-191.63878}  
Iteration 2:{space 3}log pseudolikelihood = {res:-181.13776}  
Iteration 3:{space 3}log pseudolikelihood = {res:-180.67476}  
Iteration 4:{space 3}log pseudolikelihood = {res:  -180.673}  
Iteration 5:{space 3}log pseudolikelihood = {res:  -180.673}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       533
{txt}{col 49}Wald chi2({res}6{txt}){col 67}= {res}     96.83
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}  -180.673{txt}{col 49}Pseudo R2{col 67}= {res}    0.3169

{txt}{ralign 85:(Std. Err. adjusted for {res:115} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}             punish{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}max_pers_magaloni {c |}{col 21}{res}{space 2} 1.222455{col 33}{space 2} .2268776{col 44}{space 1}    5.39{col 53}{space 3}0.000{col 61}{space 4} .7777834{col 74}{space 3} 1.667127
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2}-.0721737{col 33}{space 2} .3618373{col 44}{space 1}   -0.20{col 53}{space 3}0.842{col 61}{space 4}-.7813617{col 74}{space 3} .6370143
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2}-.1294762{col 33}{space 2}  .367516{col 44}{space 1}   -0.35{col 53}{space 3}0.725{col 61}{space 4}-.8497944{col 74}{space 3}  .590842
{txt}{space 3}max_both_max_pts {c |}{col 21}{res}{space 2} .4821513{col 33}{space 2} .1159617{col 44}{space 1}    4.16{col 53}{space 3}0.000{col 61}{space 4} .2548706{col 74}{space 3} .7094321
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0270341{col 33}{space 2} .0131496{col 44}{space 1}    2.06{col 53}{space 3}0.040{col 61}{space 4} .0012613{col 74}{space 3} .0528068
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-1.025563{col 33}{space 2} .4121643{col 44}{space 1}   -2.49{col 53}{space 3}0.013{col 61}{space 4} -1.83339{col 74}{space 3}-.2177354
{txt}{space 14}_cons {c |}{col 21}{res}{space 2} -3.19213{col 33}{space 2} .6003607{col 44}{space 1}   -5.32{col 53}{space 3}0.000{col 61}{space 4}-4.368815{col 74}{space 3}-2.015444
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a11
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}   -264.481{col 42}{txt}Log-Lik Full Model:{col 69}{res}   -180.673
{txt}D(526):{col 28}{res}    361.346{col 42}{txt}LR(6):{col 69}{res}    167.617
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.000
{txt}McFadden's R2:{col 28}{res}      0.317{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.290
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.270{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.429
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.420{col 42}{txt}Efron's R2:{col 69}{res}      0.327
{txt}Variance of y*:{col 28}{res}      5.668{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.852{col 42}{txt}Adj Count R2:{col 69}{res}      0.248
{txt}AIC:{col 28}{res}      0.704{col 42}{txt}AIC*n:{col 69}{res}    375.346
{txt}BIC:{col 28}{res}  -2941.156{col 42}{txt}BIC':{col 69}{res}   -129.946
{txt}BIC used by Stata:{col 28}{res}    405.296{col 42}{txt}AIC used by Stata:{col 69}{res}    375.346
{txt}
{com}. 
. logit punish max_pers_magaloni  irr_entry  max_both_max_pts previous_sum_punish instit_control if gwf_democracy==0, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -100.4753}  
Iteration 1:{space 3}log pseudolikelihood = {res:-89.070231}  
Iteration 2:{space 3}log pseudolikelihood = {res:-89.065298}  
Iteration 3:{space 3}log pseudolikelihood = {res:-89.065298}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       145
{txt}{col 49}Wald chi2({res}5{txt}){col 67}= {res}     19.62
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0015
{txt}Log pseudolikelihood = {res}-89.065298{txt}{col 49}Pseudo R2{col 67}= {res}    0.1136

{txt}{ralign 85:(Std. Err. adjusted for {res:75} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}             punish{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}max_pers_magaloni {c |}{col 21}{res}{space 2} 1.073299{col 33}{space 2}  .375594{col 44}{space 1}    2.86{col 53}{space 3}0.004{col 61}{space 4} .3371487{col 74}{space 3}  1.80945
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2} .0509319{col 33}{space 2} .4179248{col 44}{space 1}    0.12{col 53}{space 3}0.903{col 61}{space 4}-.7681857{col 74}{space 3} .8700495
{txt}{space 3}max_both_max_pts {c |}{col 21}{res}{space 2}  .327087{col 33}{space 2} .2175308{col 44}{space 1}    1.50{col 53}{space 3}0.133{col 61}{space 4}-.0992655{col 74}{space 3} .7534394
{txt}previous_sum_punish {c |}{col 21}{res}{space 2}-.0089986{col 33}{space 2}    .0273{col 44}{space 1}   -0.33{col 53}{space 3}0.742{col 61}{space 4}-.0625056{col 74}{space 3} .0445084
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.8507832{col 33}{space 2} .4031862{col 44}{space 1}   -2.11{col 53}{space 3}0.035{col 61}{space 4}-1.641014{col 74}{space 3}-.0605528
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-2.365781{col 33}{space 2} .9931622{col 44}{space 1}   -2.38{col 53}{space 3}0.017{col 61}{space 4}-4.312343{col 74}{space 3} -.419219
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a12
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}   -100.475{col 42}{txt}Log-Lik Full Model:{col 69}{res}    -89.065
{txt}D(139):{col 28}{res}    178.131{col 42}{txt}LR(5):{col 69}{res}     22.820
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.000
{txt}McFadden's R2:{col 28}{res}      0.114{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.054
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.146{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.194
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.185{col 42}{txt}Efron's R2:{col 69}{res}      0.147
{txt}Variance of y*:{col 28}{res}      4.035{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.669{col 42}{txt}Adj Count R2:{col 69}{res}      0.324
{txt}AIC:{col 28}{res}      1.311{col 42}{txt}AIC*n:{col 69}{res}    190.131
{txt}BIC:{col 28}{res}   -513.635{col 42}{txt}BIC':{col 69}{res}      2.064
{txt}BIC used by Stata:{col 28}{res}    207.991{col 42}{txt}AIC used by Stata:{col 69}{res}    190.131
{txt}
{com}. 
{txt}end of do-file

{com}. do "C:\Users\mtr012\AppData\Local\Temp\STD00000000.tmp"
{txt}
{com}. *Table A7
. logit punish avg_pers_magaloni, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-780.88746}  
Iteration 1:{space 3}log pseudolikelihood = {res:-666.94419}  
Iteration 2:{space 3}log pseudolikelihood = {res:-662.17346}  
Iteration 3:{space 3}log pseudolikelihood = {res:-662.16505}  
Iteration 4:{space 3}log pseudolikelihood = {res:-662.16505}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,405
{txt}{col 49}Wald chi2({res}1{txt}){col 67}= {res}     82.95
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-662.16505{txt}{col 49}Pseudo R2{col 67}= {res}    0.1520

{txt}{ralign 83:(Std. Err. adjusted for {res:162} clusters in ccode)}
{hline 18}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 19}{c |}{col 31}    Robust
{col 1}           punish{col 19}{c |}      Coef.{col 31}   Std. Err.{col 43}      z{col 51}   P>|z|{col 59}     [95% Con{col 72}f. Interval]
{hline 18}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
avg_pers_magaloni {c |}{col 19}{res}{space 2} 1.366227{col 31}{space 2} .1500071{col 42}{space 1}    9.11{col 51}{space 3}0.000{col 59}{space 4} 1.072218{col 72}{space 3} 1.660235
{txt}{space 12}_cons {c |}{col 19}{res}{space 2}-1.938673{col 31}{space 2} .1609323{col 42}{space 1}  -12.05{col 51}{space 3}0.000{col 59}{space 4}-2.254094{col 72}{space 3}-1.623251
{txt}{hline 18}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a13
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}   -780.887{col 42}{txt}Log-Lik Full Model:{col 69}{res}   -662.165
{txt}D(1403):{col 28}{res}   1324.330{col 42}{txt}LR(1):{col 69}{res}    237.445
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.000
{txt}McFadden's R2:{col 28}{res}      0.152{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.149
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.155{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.232
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.208{col 42}{txt}Efron's R2:{col 69}{res}      0.170
{txt}Variance of y*:{col 28}{res}      4.155{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.770{col 42}{txt}Adj Count R2:{col 69}{res}      0.058
{txt}AIC:{col 28}{res}      0.945{col 42}{txt}AIC*n:{col 69}{res}   1328.330
{txt}BIC:{col 28}{res}  -8844.323{col 42}{txt}BIC':{col 69}{res}   -230.197
{txt}BIC used by Stata:{col 28}{res}   1338.826{col 42}{txt}AIC used by Stata:{col 69}{res}   1328.330
{txt}
{com}. 
. logit punish avg_pers_magaloni gwf_democracy irr_entry  max_both_max_pts previous_sum_punish instit_control, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-266.00598}  
Iteration 1:{space 3}log pseudolikelihood = {res:-199.43186}  
Iteration 2:{space 3}log pseudolikelihood = {res:-190.06602}  
Iteration 3:{space 3}log pseudolikelihood = {res:-189.66645}  
Iteration 4:{space 3}log pseudolikelihood = {res:-189.66526}  
Iteration 5:{space 3}log pseudolikelihood = {res:-189.66526}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       540
{txt}{col 49}Wald chi2({res}6{txt}){col 67}= {res}     86.56
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-189.66526{txt}{col 49}Pseudo R2{col 67}= {res}    0.2870

{txt}{ralign 85:(Std. Err. adjusted for {res:117} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}             punish{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}avg_pers_magaloni {c |}{col 21}{res}{space 2} 1.083592{col 33}{space 2} .3542415{col 44}{space 1}    3.06{col 53}{space 3}0.002{col 61}{space 4} .3892909{col 74}{space 3} 1.777892
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2}-.2843663{col 33}{space 2} .4292737{col 44}{space 1}   -0.66{col 53}{space 3}0.508{col 61}{space 4}-1.125727{col 74}{space 3} .5569947
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2}-.1591484{col 33}{space 2} .3916852{col 44}{space 1}   -0.41{col 53}{space 3}0.685{col 61}{space 4}-.9268373{col 74}{space 3} .6085406
{txt}{space 3}max_both_max_pts {c |}{col 21}{res}{space 2} .5478607{col 33}{space 2} .1127644{col 44}{space 1}    4.86{col 53}{space 3}0.000{col 61}{space 4} .3268465{col 74}{space 3} .7688749
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0262197{col 33}{space 2} .0134164{col 44}{space 1}    1.95{col 53}{space 3}0.051{col 61}{space 4} -.000076{col 74}{space 3} .0525154
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-1.044551{col 33}{space 2} .4103961{col 44}{space 1}   -2.55{col 53}{space 3}0.011{col 61}{space 4}-1.848913{col 74}{space 3}-.2401896
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-2.978284{col 33}{space 2} .6415284{col 44}{space 1}   -4.64{col 53}{space 3}0.000{col 61}{space 4}-4.235657{col 74}{space 3}-1.720911
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a14
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}   -266.006{col 42}{txt}Log-Lik Full Model:{col 69}{res}   -189.665
{txt}D(533):{col 28}{res}    379.331{col 42}{txt}LR(6):{col 69}{res}    152.681
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.000
{txt}McFadden's R2:{col 28}{res}      0.287{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.261
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.246{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.393
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.397{col 42}{txt}Efron's R2:{col 69}{res}      0.287
{txt}Variance of y*:{col 28}{res}      5.458{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.844{col 42}{txt}Adj Count R2:{col 69}{res}      0.200
{txt}AIC:{col 28}{res}      0.728{col 42}{txt}AIC*n:{col 69}{res}    393.331
{txt}BIC:{col 28}{res}  -2974.076{col 42}{txt}BIC':{col 69}{res}   -114.932
{txt}BIC used by Stata:{col 28}{res}    423.371{col 42}{txt}AIC used by Stata:{col 69}{res}    393.331
{txt}
{com}. 
. logit punish avg_pers_magaloni  irr_entry  max_both_max_pts previous_sum_punish instit_control if gwf_democracy==0, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-101.14469}  
Iteration 1:{space 3}log pseudolikelihood = {res:-92.463961}  
Iteration 2:{space 3}log pseudolikelihood = {res:-92.454484}  
Iteration 3:{space 3}log pseudolikelihood = {res:-92.454483}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       146
{txt}{col 49}Wald chi2({res}5{txt}){col 67}= {res}     14.14
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0147
{txt}Log pseudolikelihood = {res}-92.454483{txt}{col 49}Pseudo R2{col 67}= {res}    0.0859

{txt}{ralign 85:(Std. Err. adjusted for {res:76} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}             punish{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}avg_pers_magaloni {c |}{col 21}{res}{space 2}  .764592{col 33}{space 2} .3988632{col 44}{space 1}    1.92{col 53}{space 3}0.055{col 61}{space 4}-.0171654{col 74}{space 3} 1.546349
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2} .1303818{col 33}{space 2} .4295424{col 44}{space 1}    0.30{col 53}{space 3}0.761{col 61}{space 4}-.7115058{col 74}{space 3} .9722694
{txt}{space 3}max_both_max_pts {c |}{col 21}{res}{space 2} .3099071{col 33}{space 2} .2113135{col 44}{space 1}    1.47{col 53}{space 3}0.142{col 61}{space 4}-.1042598{col 74}{space 3} .7240741
{txt}previous_sum_punish {c |}{col 21}{res}{space 2}-.0078036{col 33}{space 2} .0289383{col 44}{space 1}   -0.27{col 53}{space 3}0.787{col 61}{space 4}-.0645217{col 74}{space 3} .0489144
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.8340495{col 33}{space 2} .3926491{col 44}{space 1}   -2.12{col 53}{space 3}0.034{col 61}{space 4}-1.603628{col 74}{space 3}-.0644714
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-1.745905{col 33}{space 2} .9116335{col 44}{space 1}   -1.92{col 53}{space 3}0.055{col 61}{space 4}-3.532674{col 74}{space 3} .0408635
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a15
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}   -101.145{col 42}{txt}Log-Lik Full Model:{col 69}{res}    -92.454
{txt}D(140):{col 28}{res}    184.909{col 42}{txt}LR(5):{col 69}{res}     17.380
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.004
{txt}McFadden's R2:{col 28}{res}      0.086{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.027
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.112{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.150
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.142{col 42}{txt}Efron's R2:{col 69}{res}      0.115
{txt}Variance of y*:{col 28}{res}      3.835{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.630{col 42}{txt}Adj Count R2:{col 69}{res}      0.239
{txt}AIC:{col 28}{res}      1.349{col 42}{txt}AIC*n:{col 69}{res}    196.909
{txt}BIC:{col 28}{res}   -512.796{col 42}{txt}BIC':{col 69}{res}      7.538
{txt}BIC used by Stata:{col 28}{res}    214.811{col 42}{txt}AIC used by Stata:{col 69}{res}    196.909
{txt}
{com}. 
{txt}end of do-file

{com}. do "C:\Users\mtr012\AppData\Local\Temp\STD00000000.tmp"
{txt}
{com}. *Table A8
. 
. logit punish gwf_monarch gwf_military gwf_party gwf_democracy if pers_hybrid==0 & mil_hybrid==0, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-595.19614}  
Iteration 1:{space 3}log pseudolikelihood = {res:-509.70527}  
Iteration 2:{space 3}log pseudolikelihood = {res:-503.81354}  
Iteration 3:{space 3}log pseudolikelihood = {res:-503.78098}  
Iteration 4:{space 3}log pseudolikelihood = {res:-503.78098}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,103
{txt}{col 49}Wald chi2({res}4{txt}){col 67}= {res}    110.22
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-503.78098{txt}{col 49}Pseudo R2{col 67}= {res}    0.1536

{txt}{ralign 79:(Std. Err. adjusted for {res:132} clusters in ccode)}
{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}       punish{col 15}{c |}      Coef.{col 27}   Std. Err.{col 39}      z{col 47}   P>|z|{col 55}     [95% Con{col 68}f. Interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}gwf_monarch {c |}{col 15}{res}{space 2}-.2558069{col 27}{space 2} .6074763{col 38}{space 1}   -0.42{col 47}{space 3}0.674{col 55}{space 4}-1.446439{col 68}{space 3} .9348248
{txt}{space 1}gwf_military {c |}{col 15}{res}{space 2}-.6542544{col 27}{space 2} .2809676{col 38}{space 1}   -2.33{col 47}{space 3}0.020{col 55}{space 4}-1.204941{col 68}{space 3} -.103568
{txt}{space 4}gwf_party {c |}{col 15}{res}{space 2}-1.548292{col 27}{space 2} .2498339{col 38}{space 1}   -6.20{col 47}{space 3}0.000{col 55}{space 4}-2.037958{col 68}{space 3}-1.058627
{txt}gwf_democracy {c |}{col 15}{res}{space 2}-2.486539{col 27}{space 2} .2523724{col 38}{space 1}   -9.85{col 47}{space 3}0.000{col 55}{space 4} -2.98118{col 68}{space 3}-1.991898
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} .3989077{col 27}{space 2} .1549086{col 38}{space 1}    2.58{col 47}{space 3}0.010{col 55}{space 4} .0952924{col 68}{space 3}  .702523
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a16
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}   -595.196{col 42}{txt}Log-Lik Full Model:{col 69}{res}   -503.781
{txt}D(1098):{col 28}{res}   1007.562{col 42}{txt}LR(4):{col 69}{res}    182.830
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.000
{txt}McFadden's R2:{col 28}{res}      0.154{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.145
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.153{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.231
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.212{col 42}{txt}Efron's R2:{col 69}{res}      0.180
{txt}Variance of y*:{col 28}{res}      4.176{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.794{col 42}{txt}Adj Count R2:{col 69}{res}      0.106
{txt}AIC:{col 28}{res}      0.923{col 42}{txt}AIC*n:{col 69}{res}   1017.562
{txt}BIC:{col 28}{res}  -6684.794{col 42}{txt}BIC':{col 69}{res}   -154.807
{txt}BIC used by Stata:{col 28}{res}   1042.591{col 42}{txt}AIC used by Stata:{col 69}{res}   1017.562
{txt}
{com}. 
. logit punish gwf_monarch gwf_military gwf_party gwf_democracy irr_entry max_purges previous_sum_punish instit_control if pers_hybrid==0 & mil_hybrid==0, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-426.25698}  
Iteration 1:{space 3}log pseudolikelihood = {res:-348.02078}  
Iteration 2:{space 3}log pseudolikelihood = {res:-341.93648}  
Iteration 3:{space 3}log pseudolikelihood = {res:-341.20259}  
Iteration 4:{space 3}log pseudolikelihood = {res:-341.20018}  
Iteration 5:{space 3}log pseudolikelihood = {res:-341.20018}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       867
{txt}{col 49}Wald chi2({res}8{txt}){col 67}= {res}     95.23
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-341.20018{txt}{col 49}Pseudo R2{col 67}= {res}    0.1995

{txt}{ralign 85:(Std. Err. adjusted for {res:122} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}             punish{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}gwf_monarch {c |}{col 21}{res}{space 2}-.1084999{col 33}{space 2} .6874092{col 44}{space 1}   -0.16{col 53}{space 3}0.875{col 61}{space 4}-1.455797{col 74}{space 3} 1.238797
{txt}{space 7}gwf_military {c |}{col 21}{res}{space 2} -2.09026{col 33}{space 2} .5812384{col 44}{space 1}   -3.60{col 53}{space 3}0.000{col 61}{space 4}-3.229466{col 74}{space 3}-.9510534
{txt}{space 10}gwf_party {c |}{col 21}{res}{space 2}-1.413035{col 33}{space 2} .4062042{col 44}{space 1}   -3.48{col 53}{space 3}0.001{col 61}{space 4}-2.209181{col 74}{space 3}-.6168896
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2}-2.431362{col 33}{space 2} .4210207{col 44}{space 1}   -5.77{col 53}{space 3}0.000{col 61}{space 4}-3.256547{col 74}{space 3}-1.606176
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2} .7433398{col 33}{space 2} .3470826{col 44}{space 1}    2.14{col 53}{space 3}0.032{col 61}{space 4} .0630704{col 74}{space 3} 1.423609
{txt}{space 9}max_purges {c |}{col 21}{res}{space 2} .1702299{col 33}{space 2} .1932288{col 44}{space 1}    0.88{col 53}{space 3}0.378{col 61}{space 4}-.2084916{col 74}{space 3} .5489514
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0545386{col 33}{space 2} .0136077{col 44}{space 1}    4.01{col 53}{space 3}0.000{col 61}{space 4}  .027868{col 74}{space 3} .0812091
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.5779381{col 33}{space 2} .3570214{col 44}{space 1}   -1.62{col 53}{space 3}0.105{col 61}{space 4}-1.277687{col 74}{space 3}  .121811
{txt}{space 14}_cons {c |}{col 21}{res}{space 2} .5514729{col 33}{space 2}  .467264{col 44}{space 1}    1.18{col 53}{space 3}0.238{col 61}{space 4}-.3643477{col 74}{space 3} 1.467293
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a17
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}   -426.257{col 42}{txt}Log-Lik Full Model:{col 69}{res}   -341.200
{txt}D(858):{col 28}{res}    682.400{col 42}{txt}LR(8):{col 69}{res}    170.114
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.000
{txt}McFadden's R2:{col 28}{res}      0.200{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.178
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.178{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.285
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.256{col 42}{txt}Efron's R2:{col 69}{res}      0.231
{txt}Variance of y*:{col 28}{res}      4.425{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.840{col 42}{txt}Adj Count R2:{col 69}{res}      0.173
{txt}AIC:{col 28}{res}      0.808{col 42}{txt}AIC*n:{col 69}{res}    700.400
{txt}BIC:{col 28}{res}  -5122.003{col 42}{txt}BIC':{col 69}{res}   -115.993
{txt}BIC used by Stata:{col 28}{res}    743.286{col 42}{txt}AIC used by Stata:{col 69}{res}    700.400
{txt}
{com}. test gwf_monarch=gwf_party

{p 0 7}{space 1}{text:( 1)}{space 1} {res}[punish]gwf_monarch - [punish]gwf_party = 0{p_end}

{txt}{col 12}chi2(  1) ={res}    3.37
{txt}{col 10}Prob > chi2 =  {res}  0.0662
{txt}
{com}. test gwf_monarch=gwf_military

{p 0 7}{space 1}{text:( 1)}{space 1} {res}[punish]gwf_monarch - [punish]gwf_military = 0{p_end}

{txt}{col 12}chi2(  1) ={res}    7.16
{txt}{col 10}Prob > chi2 =  {res}  0.0075
{txt}
{com}. test gwf_monarch=gwf_democracy

{p 0 7}{space 1}{text:( 1)}{space 1} {res}[punish]gwf_monarch - [punish]gwf_democracy = 0{p_end}

{txt}{col 12}chi2(  1) ={res}   11.71
{txt}{col 10}Prob > chi2 =  {res}  0.0006
{txt}
{com}. 
. 
. logit punish gwf_monarch gwf_military gwf_party irr_entry  max_purges previous_sum_punish instit_control  if pers_hybrid==0 & mil_hybrid==0& gwf_democracy==0, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-131.60321}  
Iteration 1:{space 3}log pseudolikelihood = {res:-114.99679}  
Iteration 2:{space 3}log pseudolikelihood = {res:-114.96604}  
Iteration 3:{space 3}log pseudolikelihood = {res:-114.96604}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       190
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}     26.97
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0003
{txt}Log pseudolikelihood = {res}-114.96604{txt}{col 49}Pseudo R2{col 67}= {res}    0.1264

{txt}{ralign 85:(Std. Err. adjusted for {res:78} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}             punish{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}gwf_monarch {c |}{col 21}{res}{space 2}-.4331051{col 33}{space 2} .6630214{col 44}{space 1}   -0.65{col 53}{space 3}0.514{col 61}{space 4}-1.732603{col 74}{space 3}  .866393
{txt}{space 7}gwf_military {c |}{col 21}{res}{space 2}-1.794189{col 33}{space 2} .4875329{col 44}{space 1}   -3.68{col 53}{space 3}0.000{col 61}{space 4}-2.749735{col 74}{space 3}-.8386417
{txt}{space 10}gwf_party {c |}{col 21}{res}{space 2}-1.580015{col 33}{space 2}  .384255{col 44}{space 1}   -4.11{col 53}{space 3}0.000{col 61}{space 4}-2.333141{col 74}{space 3}-.8268894
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2} .6950383{col 33}{space 2} .3954377{col 44}{space 1}    1.76{col 53}{space 3}0.079{col 61}{space 4}-.0800054{col 74}{space 3} 1.470082
{txt}{space 9}max_purges {c |}{col 21}{res}{space 2}-.0153922{col 33}{space 2} .0429391{col 44}{space 1}   -0.36{col 53}{space 3}0.720{col 61}{space 4}-.0995513{col 74}{space 3} .0687668
{txt}previous_sum_punish {c |}{col 21}{res}{space 2}-.0149628{col 33}{space 2} .0188735{col 44}{space 1}   -0.79{col 53}{space 3}0.428{col 61}{space 4}-.0519542{col 74}{space 3} .0220285
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2} -.360162{col 33}{space 2} .3173851{col 44}{space 1}   -1.13{col 53}{space 3}0.256{col 61}{space 4}-.9822253{col 74}{space 3} .2619013
{txt}{space 14}_cons {c |}{col 21}{res}{space 2} 1.026787{col 33}{space 2} .4102362{col 44}{space 1}    2.50{col 53}{space 3}0.012{col 61}{space 4} .2227391{col 74}{space 3} 1.830835
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a18
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}   -131.603{col 42}{txt}Log-Lik Full Model:{col 69}{res}   -114.966
{txt}D(182):{col 28}{res}    229.932{col 42}{txt}LR(7):{col 69}{res}     33.274
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.000
{txt}McFadden's R2:{col 28}{res}      0.126{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.066
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.161{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.214
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.203{col 42}{txt}Efron's R2:{col 69}{res}      0.167
{txt}Variance of y*:{col 28}{res}      4.127{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.684{col 42}{txt}Adj Count R2:{col 69}{res}      0.348
{txt}AIC:{col 28}{res}      1.294{col 42}{txt}AIC*n:{col 69}{res}    245.932
{txt}BIC:{col 28}{res}   -725.026{col 42}{txt}BIC':{col 69}{res}      3.455
{txt}BIC used by Stata:{col 28}{res}    271.908{col 42}{txt}AIC used by Stata:{col 69}{res}    245.932
{txt}
{com}. 
{txt}end of do-file

{com}. do "C:\Users\mtr012\AppData\Local\Temp\STD00000000.tmp"
{txt}
{com}. *Table A9
. 
. logit punish strongman boss machine gwf_democracy max_purges irr_entry previous_sum_punish instit_control, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-353.92557}  
Iteration 1:{space 3}log pseudolikelihood = {res:-286.56142}  
Iteration 2:{space 3}log pseudolikelihood = {res:-279.49476}  
Iteration 3:{space 3}log pseudolikelihood = {res:-278.48092}  
Iteration 4:{space 3}log pseudolikelihood = {res:-278.47742}  
Iteration 5:{space 3}log pseudolikelihood = {res:-278.47742}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       730
{txt}{col 49}Wald chi2({res}8{txt}){col 67}= {res}     96.07
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-278.47742{txt}{col 49}Pseudo R2{col 67}= {res}    0.2132

{txt}{ralign 85:(Std. Err. adjusted for {res:113} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}             punish{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 10}strongman {c |}{col 21}{res}{space 2} 1.200856{col 33}{space 2} .4028909{col 44}{space 1}    2.98{col 53}{space 3}0.003{col 61}{space 4} .4112039{col 74}{space 3} 1.990507
{txt}{space 15}boss {c |}{col 21}{res}{space 2}  1.69753{col 33}{space 2} .7541528{col 44}{space 1}    2.25{col 53}{space 3}0.024{col 61}{space 4} .2194178{col 74}{space 3} 3.175642
{txt}{space 12}machine {c |}{col 21}{res}{space 2}-.6008485{col 33}{space 2} .4281515{col 44}{space 1}   -1.40{col 53}{space 3}0.161{col 61}{space 4} -1.44001{col 74}{space 3} .2383131
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2} -1.60815{col 33}{space 2} .3431174{col 44}{space 1}   -4.69{col 53}{space 3}0.000{col 61}{space 4}-2.280648{col 74}{space 3}-.9356521
{txt}{space 9}max_purges {c |}{col 21}{res}{space 2} .0660427{col 33}{space 2} .0985572{col 44}{space 1}    0.67{col 53}{space 3}0.503{col 61}{space 4}-.1271258{col 74}{space 3} .2592112
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2}-.2719901{col 33}{space 2} .3452016{col 44}{space 1}   -0.79{col 53}{space 3}0.431{col 61}{space 4}-.9485728{col 74}{space 3} .4045926
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0381865{col 33}{space 2} .0153104{col 44}{space 1}    2.49{col 53}{space 3}0.013{col 61}{space 4} .0081788{col 74}{space 3} .0681943
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.4910364{col 33}{space 2} .2892899{col 44}{space 1}   -1.70{col 53}{space 3}0.090{col 61}{space 4}-1.058034{col 74}{space 3} .0759615
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-.3820692{col 33}{space 2} .3927379{col 44}{space 1}   -0.97{col 53}{space 3}0.331{col 61}{space 4}-1.151821{col 74}{space 3}  .387683
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a19
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}   -353.926{col 42}{txt}Log-Lik Full Model:{col 69}{res}   -278.477
{txt}D(721):{col 28}{res}    556.955{col 42}{txt}LR(8):{col 69}{res}    150.896
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.000
{txt}McFadden's R2:{col 28}{res}      0.213{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.188
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.187{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.301
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.261{col 42}{txt}Efron's R2:{col 69}{res}      0.242
{txt}Variance of y*:{col 28}{res}      4.452{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.849{col 42}{txt}Adj Count R2:{col 69}{res}      0.203
{txt}AIC:{col 28}{res}      0.788{col 42}{txt}AIC*n:{col 69}{res}    574.955
{txt}BIC:{col 28}{res}  -4196.630{col 42}{txt}BIC':{col 69}{res}    -98.152
{txt}BIC used by Stata:{col 28}{res}    616.292{col 42}{txt}AIC used by Stata:{col 69}{res}    574.955
{txt}
{com}. 
{txt}end of do-file

{com}. do "C:\Users\mtr012\AppData\Local\Temp\STD00000000.tmp"
{txt}
{com}. *Table A10
. 
. logit punish max_person_scale max_military_scale gwf_democracy irr_entry max_both_avg_pts previous_sum_punish instit_control, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-214.23719}  
Iteration 1:{space 3}log pseudolikelihood = {res:-158.85454}  
Iteration 2:{space 3}log pseudolikelihood = {res:-150.07276}  
Iteration 3:{space 3}log pseudolikelihood = {res:-149.58344}  
Iteration 4:{space 3}log pseudolikelihood = {res:-149.58197}  
Iteration 5:{space 3}log pseudolikelihood = {res:-149.58197}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       453
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}     89.61
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-149.58197{txt}{col 49}Pseudo R2{col 67}= {res}    0.3018

{txt}{ralign 85:(Std. Err. adjusted for {res:108} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}             punish{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}max_person_scale {c |}{col 21}{res}{space 2} 1.650469{col 33}{space 2}    .4869{col 44}{space 1}    3.39{col 53}{space 3}0.001{col 61}{space 4} .6961624{col 74}{space 3} 2.604775
{txt}{space 1}max_military_scale {c |}{col 21}{res}{space 2} 1.330319{col 33}{space 2} .5074836{col 44}{space 1}    2.62{col 53}{space 3}0.009{col 61}{space 4} .3356689{col 74}{space 3} 2.324968
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2}-.8401525{col 33}{space 2} .3787348{col 44}{space 1}   -2.22{col 53}{space 3}0.027{col 61}{space 4}-1.582459{col 74}{space 3} -.097846
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2}-.8276927{col 33}{space 2} .5598982{col 44}{space 1}   -1.48{col 53}{space 3}0.139{col 61}{space 4}-1.925073{col 74}{space 3} .2696877
{txt}{space 3}max_both_avg_pts {c |}{col 21}{res}{space 2} .4711348{col 33}{space 2} .1720225{col 44}{space 1}    2.74{col 53}{space 3}0.006{col 61}{space 4} .1339769{col 74}{space 3} .8082928
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0186082{col 33}{space 2} .0140543{col 44}{space 1}    1.32{col 53}{space 3}0.185{col 61}{space 4}-.0089378{col 74}{space 3} .0461542
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.8413994{col 33}{space 2} .4548474{col 44}{space 1}   -1.85{col 53}{space 3}0.064{col 61}{space 4}-1.732884{col 74}{space 3} .0500851
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-2.424893{col 33}{space 2} .7021939{col 44}{space 1}   -3.45{col 53}{space 3}0.001{col 61}{space 4}-3.801167{col 74}{space 3}-1.048618
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a20
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}   -214.237{col 42}{txt}Log-Lik Full Model:{col 69}{res}   -149.582
{txt}D(445):{col 28}{res}    299.164{col 42}{txt}LR(7):{col 69}{res}    129.310
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.000
{txt}McFadden's R2:{col 28}{res}      0.302{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.264
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.248{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.406
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.393{col 42}{txt}Efron's R2:{col 69}{res}      0.308
{txt}Variance of y*:{col 28}{res}      5.423{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.843{col 42}{txt}Adj Count R2:{col 69}{res}      0.134
{txt}AIC:{col 28}{res}      0.696{col 42}{txt}AIC*n:{col 69}{res}    315.164
{txt}BIC:{col 28}{res}  -2422.408{col 42}{txt}BIC':{col 69}{res}    -86.499
{txt}BIC used by Stata:{col 28}{res}    348.091{col 42}{txt}AIC used by Stata:{col 69}{res}    315.164
{txt}
{com}. 
. logit punish max_person_scale max_military_scale gwf_democracy irr_entry avg_both_max_pts previous_sum_punish instit_control, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-214.23719}  
Iteration 1:{space 3}log pseudolikelihood = {res:-161.20808}  
Iteration 2:{space 3}log pseudolikelihood = {res:-153.42515}  
Iteration 3:{space 3}log pseudolikelihood = {res:-152.99549}  
Iteration 4:{space 3}log pseudolikelihood = {res:-152.99481}  
Iteration 5:{space 3}log pseudolikelihood = {res:-152.99481}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       453
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}     90.41
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-152.99481{txt}{col 49}Pseudo R2{col 67}= {res}    0.2859

{txt}{ralign 85:(Std. Err. adjusted for {res:108} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}             punish{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}max_person_scale {c |}{col 21}{res}{space 2} 1.982892{col 33}{space 2} .5020733{col 44}{space 1}    3.95{col 53}{space 3}0.000{col 61}{space 4} .9988469{col 74}{space 3} 2.966938
{txt}{space 1}max_military_scale {c |}{col 21}{res}{space 2} 1.530653{col 33}{space 2} .5130754{col 44}{space 1}    2.98{col 53}{space 3}0.003{col 61}{space 4} .5250432{col 74}{space 3} 2.536262
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2}-1.011182{col 33}{space 2} .4056167{col 44}{space 1}   -2.49{col 53}{space 3}0.013{col 61}{space 4}-1.806177{col 74}{space 3}-.2161882
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2}-.9171287{col 33}{space 2} .5638599{col 44}{space 1}   -1.63{col 53}{space 3}0.104{col 61}{space 4}-2.022274{col 74}{space 3} .1880165
{txt}{space 3}avg_both_max_pts {c |}{col 21}{res}{space 2} .2419791{col 33}{space 2} .1448894{col 44}{space 1}    1.67{col 53}{space 3}0.095{col 61}{space 4}-.0419989{col 74}{space 3} .5259571
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0199574{col 33}{space 2} .0133156{col 44}{space 1}    1.50{col 53}{space 3}0.134{col 61}{space 4}-.0061407{col 74}{space 3} .0460555
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.7138724{col 33}{space 2} .4745003{col 44}{space 1}   -1.50{col 53}{space 3}0.132{col 61}{space 4}-1.643876{col 74}{space 3} .2161311
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-1.704306{col 33}{space 2} .5928036{col 44}{space 1}   -2.87{col 53}{space 3}0.004{col 61}{space 4}-2.866179{col 74}{space 3}-.5424318
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a21
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}   -214.237{col 42}{txt}Log-Lik Full Model:{col 69}{res}   -152.995
{txt}D(445):{col 28}{res}    305.990{col 42}{txt}LR(7):{col 69}{res}    122.485
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.000
{txt}McFadden's R2:{col 28}{res}      0.286{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.249
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.237{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.387
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.354{col 42}{txt}Efron's R2:{col 69}{res}      0.297
{txt}Variance of y*:{col 28}{res}      5.090{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.861{col 42}{txt}Adj Count R2:{col 69}{res}      0.232
{txt}AIC:{col 28}{res}      0.711{col 42}{txt}AIC*n:{col 69}{res}    321.990
{txt}BIC:{col 28}{res}  -2415.582{col 42}{txt}BIC':{col 69}{res}    -79.674
{txt}BIC used by Stata:{col 28}{res}    354.917{col 42}{txt}AIC used by Stata:{col 69}{res}    321.990
{txt}
{com}. 
. logit punish max_person_scale max_military_scale gwf_democracy irr_entry avg_both_avg_pts previous_sum_punish instit_control, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-214.23719}  
Iteration 1:{space 3}log pseudolikelihood = {res:-161.32703}  
Iteration 2:{space 3}log pseudolikelihood = {res:-153.60524}  
Iteration 3:{space 3}log pseudolikelihood = {res:-153.17848}  
Iteration 4:{space 3}log pseudolikelihood = {res:-153.17782}  
Iteration 5:{space 3}log pseudolikelihood = {res:-153.17782}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       453
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}     89.89
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-153.17782{txt}{col 49}Pseudo R2{col 67}= {res}    0.2850

{txt}{ralign 85:(Std. Err. adjusted for {res:108} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}             punish{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}max_person_scale {c |}{col 21}{res}{space 2} 1.963572{col 33}{space 2} .5000763{col 44}{space 1}    3.93{col 53}{space 3}0.000{col 61}{space 4} .9834408{col 74}{space 3} 2.943704
{txt}{space 1}max_military_scale {c |}{col 21}{res}{space 2} 1.550655{col 33}{space 2} .5119814{col 44}{space 1}    3.03{col 53}{space 3}0.002{col 61}{space 4} .5471893{col 74}{space 3}  2.55412
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2}-.9986377{col 33}{space 2} .4077984{col 44}{space 1}   -2.45{col 53}{space 3}0.014{col 61}{space 4}-1.797908{col 74}{space 3}-.1993675
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2}-.8949724{col 33}{space 2} .5622109{col 44}{space 1}   -1.59{col 53}{space 3}0.111{col 61}{space 4}-1.996886{col 74}{space 3} .2069407
{txt}{space 3}avg_both_avg_pts {c |}{col 21}{res}{space 2} .2390832{col 33}{space 2} .1545139{col 44}{space 1}    1.55{col 53}{space 3}0.122{col 61}{space 4}-.0637585{col 74}{space 3} .5419249
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0209517{col 33}{space 2} .0131217{col 44}{space 1}    1.60{col 53}{space 3}0.110{col 61}{space 4}-.0047664{col 74}{space 3} .0466698
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.6913486{col 33}{space 2} .4693306{col 44}{space 1}   -1.47{col 53}{space 3}0.141{col 61}{space 4} -1.61122{col 74}{space 3} .2285226
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-1.687053{col 33}{space 2} .6038965{col 44}{space 1}   -2.79{col 53}{space 3}0.005{col 61}{space 4}-2.870669{col 74}{space 3}-.5034378
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a22
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}   -214.237{col 42}{txt}Log-Lik Full Model:{col 69}{res}   -153.178
{txt}D(445):{col 28}{res}    306.356{col 42}{txt}LR(7):{col 69}{res}    122.119
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.000
{txt}McFadden's R2:{col 28}{res}      0.285{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.248
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.236{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.386
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.351{col 42}{txt}Efron's R2:{col 69}{res}      0.296
{txt}Variance of y*:{col 28}{res}      5.072{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.859{col 42}{txt}Adj Count R2:{col 69}{res}      0.220
{txt}AIC:{col 28}{res}      0.712{col 42}{txt}AIC*n:{col 69}{res}    322.356
{txt}BIC:{col 28}{res}  -2415.216{col 42}{txt}BIC':{col 69}{res}    -79.307
{txt}BIC used by Stata:{col 28}{res}    355.283{col 42}{txt}AIC used by Stata:{col 69}{res}    322.356
{txt}
{com}. 
{txt}end of do-file

{com}. do "C:\Users\mtr012\AppData\Local\Temp\STD00000000.tmp"
{txt}
{com}. *Table A11
. logit punish gwf_personalist irr_entry max_ciri previous_sum_punish instit_control, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-207.61036}  
Iteration 1:{space 3}log pseudolikelihood = {res:-164.86807}  
Iteration 2:{space 3}log pseudolikelihood = {res:-160.38831}  
Iteration 3:{space 3}log pseudolikelihood = {res:-159.09532}  
Iteration 4:{space 3}log pseudolikelihood = {res:-159.09413}  
Iteration 5:{space 3}log pseudolikelihood = {res:-159.09413}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       459
{txt}{col 49}Wald chi2({res}5{txt}){col 67}= {res}     78.78
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-159.09413{txt}{col 49}Pseudo R2{col 67}= {res}    0.2337

{txt}{ralign 85:(Std. Err. adjusted for {res:114} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}             punish{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}gwf_personalist {c |}{col 21}{res}{space 2} 2.055117{col 33}{space 2} .5794359{col 44}{space 1}    3.55{col 53}{space 3}0.000{col 61}{space 4} .9194434{col 74}{space 3}  3.19079
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2} 1.131086{col 33}{space 2} .3727638{col 44}{space 1}    3.03{col 53}{space 3}0.002{col 61}{space 4} .4004822{col 74}{space 3} 1.861689
{txt}{space 11}max_ciri {c |}{col 21}{res}{space 2}-.1680968{col 33}{space 2} .0637928{col 44}{space 1}   -2.64{col 53}{space 3}0.008{col 61}{space 4}-.2931284{col 74}{space 3}-.0430652
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0426738{col 33}{space 2} .0127051{col 44}{space 1}    3.36{col 53}{space 3}0.001{col 61}{space 4} .0177722{col 74}{space 3} .0675754
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-1.436514{col 33}{space 2} .5153803{col 44}{space 1}   -2.79{col 53}{space 3}0.005{col 61}{space 4}-2.446641{col 74}{space 3} -.426387
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-.1640603{col 33}{space 2} .6658184{col 44}{space 1}   -0.25{col 53}{space 3}0.805{col 61}{space 4} -1.46904{col 74}{space 3}  1.14092
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a23
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}   -207.610{col 42}{txt}Log-Lik Full Model:{col 69}{res}   -159.094
{txt}D(453):{col 28}{res}    318.188{col 42}{txt}LR(5):{col 69}{res}     97.032
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.000
{txt}McFadden's R2:{col 28}{res}      0.234{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.205
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.191{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.320
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.291{col 42}{txt}Efron's R2:{col 69}{res}      0.255
{txt}Variance of y*:{col 28}{res}      4.640{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.867{col 42}{txt}Adj Count R2:{col 69}{res}      0.208
{txt}AIC:{col 28}{res}      0.719{col 42}{txt}AIC*n:{col 69}{res}    330.188
{txt}BIC:{col 28}{res}  -2458.271{col 42}{txt}BIC':{col 69}{res}    -66.387
{txt}BIC used by Stata:{col 28}{res}    354.963{col 42}{txt}AIC used by Stata:{col 69}{res}    330.188
{txt}
{com}. 
. logit punish max_person_scale max_military_scale gwf_democracy irr_entry max_ciri previous_sum_punish instit_control, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:  -166.764}  
Iteration 1:{space 3}log pseudolikelihood = {res:-128.87562}  
Iteration 2:{space 3}log pseudolikelihood = {res:-120.26282}  
Iteration 3:{space 3}log pseudolikelihood = {res:-118.91003}  
Iteration 4:{space 3}log pseudolikelihood = {res:-118.90652}  
Iteration 5:{space 3}log pseudolikelihood = {res:-118.90652}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       386
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}     79.94
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-118.90652{txt}{col 49}Pseudo R2{col 67}= {res}    0.2870

{txt}{ralign 85:(Std. Err. adjusted for {res:104} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}             punish{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}max_person_scale {c |}{col 21}{res}{space 2} 1.997897{col 33}{space 2} .7070601{col 44}{space 1}    2.83{col 53}{space 3}0.005{col 61}{space 4}  .612085{col 74}{space 3}  3.38371
{txt}{space 1}max_military_scale {c |}{col 21}{res}{space 2} 1.449117{col 33}{space 2} .6547148{col 44}{space 1}    2.21{col 53}{space 3}0.027{col 61}{space 4} .1658999{col 74}{space 3} 2.732335
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2}-1.133513{col 33}{space 2} .4640267{col 44}{space 1}   -2.44{col 53}{space 3}0.015{col 61}{space 4}-2.042989{col 74}{space 3}-.2240377
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2}-.8453763{col 33}{space 2}  .634218{col 44}{space 1}   -1.33{col 53}{space 3}0.183{col 61}{space 4}-2.088421{col 74}{space 3} .3976682
{txt}{space 11}max_ciri {c |}{col 21}{res}{space 2}-.1681179{col 33}{space 2} .0782003{col 44}{space 1}   -2.15{col 53}{space 3}0.032{col 61}{space 4}-.3213877{col 74}{space 3}-.0148482
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0334659{col 33}{space 2} .0169514{col 44}{space 1}    1.97{col 53}{space 3}0.048{col 61}{space 4} .0002418{col 74}{space 3}   .06669
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.4796994{col 33}{space 2} .7240818{col 44}{space 1}   -0.66{col 53}{space 3}0.508{col 61}{space 4}-1.898874{col 74}{space 3} .9394748
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-.4604698{col 33}{space 2}  .962769{col 44}{space 1}   -0.48{col 53}{space 3}0.632{col 61}{space 4}-2.347462{col 74}{space 3} 1.426523
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a24
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}   -166.764{col 42}{txt}Log-Lik Full Model:{col 69}{res}   -118.907
{txt}D(378):{col 28}{res}    237.813{col 42}{txt}LR(7):{col 69}{res}     95.715
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.000
{txt}McFadden's R2:{col 28}{res}      0.287{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.239
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.220{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.380
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.347{col 42}{txt}Efron's R2:{col 69}{res}      0.277
{txt}Variance of y*:{col 28}{res}      5.040{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.868{col 42}{txt}Adj Count R2:{col 69}{res}      0.150
{txt}AIC:{col 28}{res}      0.658{col 42}{txt}AIC*n:{col 69}{res}    253.813
{txt}BIC:{col 28}{res}  -2013.493{col 42}{txt}BIC':{col 69}{res}    -54.024
{txt}BIC used by Stata:{col 28}{res}    285.460{col 42}{txt}AIC used by Stata:{col 69}{res}    253.813
{txt}
{com}. 
. logit punish max_pers_magaloni max_military_scale gwf_democracy irr_entry max_ciri  previous_sum_punish instit_control, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-172.28633}  
Iteration 1:{space 3}log pseudolikelihood = {res:-127.01584}  
Iteration 2:{space 3}log pseudolikelihood = {res:-118.27156}  
Iteration 3:{space 3}log pseudolikelihood = {res:-116.35617}  
Iteration 4:{space 3}log pseudolikelihood = {res:-116.34906}  
Iteration 5:{space 3}log pseudolikelihood = {res:-116.34906}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       389
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}     76.93
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-116.34906{txt}{col 49}Pseudo R2{col 67}= {res}    0.3247

{txt}{ralign 85:(Std. Err. adjusted for {res:103} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}             punish{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}max_pers_magaloni {c |}{col 21}{res}{space 2} 1.606631{col 33}{space 2} .4059622{col 44}{space 1}    3.96{col 53}{space 3}0.000{col 61}{space 4} .8109593{col 74}{space 3} 2.402302
{txt}{space 1}max_military_scale {c |}{col 21}{res}{space 2}  .716697{col 33}{space 2} .7098701{col 44}{space 1}    1.01{col 53}{space 3}0.313{col 61}{space 4}-.6746228{col 74}{space 3} 2.108017
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2}-.0975595{col 33}{space 2} .5563023{col 44}{space 1}   -0.18{col 53}{space 3}0.861{col 61}{space 4}-1.187892{col 74}{space 3}  .992773
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2}-.5781599{col 33}{space 2} .5811787{col 44}{space 1}   -0.99{col 53}{space 3}0.320{col 61}{space 4}-1.717249{col 74}{space 3} .5609295
{txt}{space 11}max_ciri {c |}{col 21}{res}{space 2}-.1442545{col 33}{space 2} .0773372{col 44}{space 1}   -1.87{col 53}{space 3}0.062{col 61}{space 4}-.2958325{col 74}{space 3} .0073236
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0249067{col 33}{space 2} .0210519{col 44}{space 1}    1.18{col 53}{space 3}0.237{col 61}{space 4}-.0163542{col 74}{space 3} .0661676
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.5610247{col 33}{space 2} .7021664{col 44}{space 1}   -0.80{col 53}{space 3}0.424{col 61}{space 4}-1.937245{col 74}{space 3} .8151961
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-1.616658{col 33}{space 2} 1.134324{col 44}{space 1}   -1.43{col 53}{space 3}0.154{col 61}{space 4}-3.839892{col 74}{space 3} .6065764
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a25
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}   -172.286{col 42}{txt}Log-Lik Full Model:{col 69}{res}   -116.349
{txt}D(381):{col 28}{res}    232.698{col 42}{txt}LR(7):{col 69}{res}    111.875
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.000
{txt}McFadden's R2:{col 28}{res}      0.325{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.278
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.250{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.425
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.378{col 42}{txt}Efron's R2:{col 69}{res}      0.326
{txt}Variance of y*:{col 28}{res}      5.293{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.877{col 42}{txt}Adj Count R2:{col 69}{res}      0.238
{txt}AIC:{col 28}{res}      0.639{col 42}{txt}AIC*n:{col 69}{res}    248.698
{txt}BIC:{col 28}{res}  -2039.426{col 42}{txt}BIC':{col 69}{res}    -70.129
{txt}BIC used by Stata:{col 28}{res}    280.407{col 42}{txt}AIC used by Stata:{col 69}{res}    248.698
{txt}
{com}. 
{txt}end of do-file

{com}. do "C:\Users\mtr012\AppData\Local\Temp\STD00000000.tmp"
{txt}
{com}. *Table A12
. logit punish gwf_personalist irr_entry max_purges  previous_sum_punish max_oppos_nelda, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-649.19508}  
Iteration 1:{space 3}log pseudolikelihood = {res: -569.9025}  
Iteration 2:{space 3}log pseudolikelihood = {res: -566.8354}  
Iteration 3:{space 3}log pseudolikelihood = {res:-566.82821}  
Iteration 4:{space 3}log pseudolikelihood = {res:-566.82821}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}     1,153
{txt}{col 49}Wald chi2({res}5{txt}){col 67}= {res}     88.16
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-566.82821{txt}{col 49}Pseudo R2{col 67}= {res}    0.1269

{txt}{ralign 85:(Std. Err. adjusted for {res:134} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}             punish{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}gwf_personalist {c |}{col 21}{res}{space 2} 1.343209{col 33}{space 2} .2212675{col 44}{space 1}    6.07{col 53}{space 3}0.000{col 61}{space 4} .9095323{col 74}{space 3} 1.776885
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2} .9482222{col 33}{space 2} .2080661{col 44}{space 1}    4.56{col 53}{space 3}0.000{col 61}{space 4} .5404201{col 74}{space 3} 1.356024
{txt}{space 9}max_purges {c |}{col 21}{res}{space 2} .2211763{col 33}{space 2} .2135688{col 44}{space 1}    1.04{col 53}{space 3}0.300{col 61}{space 4}-.1974108{col 74}{space 3} .6397634
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0345689{col 33}{space 2} .0119317{col 44}{space 1}    2.90{col 53}{space 3}0.004{col 61}{space 4} .0111831{col 74}{space 3} .0579546
{txt}{space 4}max_oppos_nelda {c |}{col 21}{res}{space 2}-.3718822{col 33}{space 2} .1840866{col 44}{space 1}   -2.02{col 53}{space 3}0.043{col 61}{space 4}-.7326852{col 74}{space 3}-.0110791
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-1.606993{col 33}{space 2} .2713615{col 44}{space 1}   -5.92{col 53}{space 3}0.000{col 61}{space 4}-2.138851{col 74}{space 3}-1.075134
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a26
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}   -649.195{col 42}{txt}Log-Lik Full Model:{col 69}{res}   -566.828
{txt}D(1147):{col 28}{res}   1133.656{col 42}{txt}LR(5):{col 69}{res}    164.734
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.000
{txt}McFadden's R2:{col 28}{res}      0.127{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.118
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.133{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.197
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.199{col 42}{txt}Efron's R2:{col 69}{res}      0.165
{txt}Variance of y*:{col 28}{res}      4.108{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.772{col 42}{txt}Adj Count R2:{col 69}{res}      0.090
{txt}AIC:{col 28}{res}      0.994{col 42}{txt}AIC*n:{col 69}{res}   1145.656
{txt}BIC:{col 28}{res}  -6952.834{col 42}{txt}BIC':{col 69}{res}   -129.483
{txt}BIC used by Stata:{col 28}{res}   1175.957{col 42}{txt}AIC used by Stata:{col 69}{res}   1145.656
{txt}
{com}. 
. logit punish max_person_scale max_military_scale gwf_democracy irr_entry max_purges previous_sum_punish max_oppos_nelda, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-456.38486}  
Iteration 1:{space 3}log pseudolikelihood = {res:-338.64819}  
Iteration 2:{space 3}log pseudolikelihood = {res:-328.70818}  
Iteration 3:{space 3}log pseudolikelihood = {res:-328.50743}  
Iteration 4:{space 3}log pseudolikelihood = {res:-328.50729}  
Iteration 5:{space 3}log pseudolikelihood = {res:-328.50729}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       869
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}    186.93
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-328.50729{txt}{col 49}Pseudo R2{col 67}= {res}    0.2802

{txt}{ralign 85:(Std. Err. adjusted for {res:119} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}             punish{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}max_person_scale {c |}{col 21}{res}{space 2} 2.068686{col 33}{space 2} .2943512{col 44}{space 1}    7.03{col 53}{space 3}0.000{col 61}{space 4} 1.491768{col 74}{space 3} 2.645603
{txt}{space 1}max_military_scale {c |}{col 21}{res}{space 2} 1.704856{col 33}{space 2}  .288158{col 44}{space 1}    5.92{col 53}{space 3}0.000{col 61}{space 4} 1.140077{col 74}{space 3} 2.269636
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2}-.5168958{col 33}{space 2} .2724779{col 44}{space 1}   -1.90{col 53}{space 3}0.058{col 61}{space 4}-1.050943{col 74}{space 3} .0171511
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2}-.1280343{col 33}{space 2} .3415269{col 44}{space 1}   -0.37{col 53}{space 3}0.708{col 61}{space 4}-.7974147{col 74}{space 3} .5413461
{txt}{space 9}max_purges {c |}{col 21}{res}{space 2} .0319668{col 33}{space 2} .0661234{col 44}{space 1}    0.48{col 53}{space 3}0.629{col 61}{space 4}-.0976327{col 74}{space 3} .1615664
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0373961{col 33}{space 2} .0113589{col 44}{space 1}    3.29{col 53}{space 3}0.001{col 61}{space 4}  .015133{col 74}{space 3} .0596591
{txt}{space 4}max_oppos_nelda {c |}{col 21}{res}{space 2}-.0656102{col 33}{space 2} .2643282{col 44}{space 1}   -0.25{col 53}{space 3}0.804{col 61}{space 4}-.5836839{col 74}{space 3} .4524636
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-2.271923{col 33}{space 2}  .307392{col 44}{space 1}   -7.39{col 53}{space 3}0.000{col 61}{space 4}-2.874401{col 74}{space 3}-1.669446
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a27
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}   -456.385{col 42}{txt}Log-Lik Full Model:{col 69}{res}   -328.507
{txt}D(861):{col 28}{res}    657.015{col 42}{txt}LR(7):{col 69}{res}    255.755
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.000
{txt}McFadden's R2:{col 28}{res}      0.280{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.263
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.255{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.392
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.358{col 42}{txt}Efron's R2:{col 69}{res}      0.297
{txt}Variance of y*:{col 28}{res}      5.127{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.818{col 42}{txt}Adj Count R2:{col 69}{res}      0.168
{txt}AIC:{col 28}{res}      0.774{col 42}{txt}AIC*n:{col 69}{res}    673.015
{txt}BIC:{col 28}{res}  -5169.668{col 42}{txt}BIC':{col 69}{res}   -208.384
{txt}BIC used by Stata:{col 28}{res}    711.153{col 42}{txt}AIC used by Stata:{col 69}{res}    673.015
{txt}
{com}. 
. logit punish max_pers_magaloni max_military_scale gwf_democracy irr_entry max_purges  previous_sum_punish max_oppos_nelda, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-467.87679}  
Iteration 1:{space 3}log pseudolikelihood = {res:-340.27198}  
Iteration 2:{space 3}log pseudolikelihood = {res:-329.62569}  
Iteration 3:{space 3}log pseudolikelihood = {res: -329.3708}  
Iteration 4:{space 3}log pseudolikelihood = {res:-329.37039}  
Iteration 5:{space 3}log pseudolikelihood = {res:-329.37039}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       861
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}    127.72
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-329.37039{txt}{col 49}Pseudo R2{col 67}= {res}    0.2960

{txt}{ralign 85:(Std. Err. adjusted for {res:121} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}             punish{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}max_pers_magaloni {c |}{col 21}{res}{space 2} 1.517217{col 33}{space 2}  .206961{col 44}{space 1}    7.33{col 53}{space 3}0.000{col 61}{space 4}  1.11158{col 74}{space 3} 1.922853
{txt}{space 1}max_military_scale {c |}{col 21}{res}{space 2} .4406064{col 33}{space 2} .3398951{col 44}{space 1}    1.30{col 53}{space 3}0.195{col 61}{space 4}-.2255757{col 74}{space 3} 1.106788
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2} .0130188{col 33}{space 2} .2849375{col 44}{space 1}    0.05{col 53}{space 3}0.964{col 61}{space 4}-.5454484{col 74}{space 3} .5714859
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2} .1396518{col 33}{space 2} .3283177{col 44}{space 1}    0.43{col 53}{space 3}0.671{col 61}{space 4}-.5038391{col 74}{space 3} .7831427
{txt}{space 9}max_purges {c |}{col 21}{res}{space 2} .0072843{col 33}{space 2}  .087745{col 44}{space 1}    0.08{col 53}{space 3}0.934{col 61}{space 4}-.1646927{col 74}{space 3} .1792613
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0172421{col 33}{space 2} .0129917{col 44}{space 1}    1.33{col 53}{space 3}0.184{col 61}{space 4}-.0082212{col 74}{space 3} .0427054
{txt}{space 4}max_oppos_nelda {c |}{col 21}{res}{space 2}-.1187421{col 33}{space 2} .2618535{col 44}{space 1}   -0.45{col 53}{space 3}0.650{col 61}{space 4}-.6319654{col 74}{space 3} .3944813
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-2.775927{col 33}{space 2} .3543546{col 44}{space 1}   -7.83{col 53}{space 3}0.000{col 61}{space 4} -3.47045{col 74}{space 3}-2.081405
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a28
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}   -467.877{col 42}{txt}Log-Lik Full Model:{col 69}{res}   -329.370
{txt}D(853):{col 28}{res}    658.741{col 42}{txt}LR(7):{col 69}{res}    277.013
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.000
{txt}McFadden's R2:{col 28}{res}      0.296{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.279
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.275{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.415
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.386{col 42}{txt}Efron's R2:{col 69}{res}      0.313
{txt}Variance of y*:{col 28}{res}      5.356{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.822{col 42}{txt}Adj Count R2:{col 69}{res}      0.239
{txt}AIC:{col 28}{res}      0.784{col 42}{txt}AIC*n:{col 69}{res}    674.741
{txt}BIC:{col 28}{res}  -5105.914{col 42}{txt}BIC':{col 69}{res}   -229.706
{txt}BIC used by Stata:{col 28}{res}    712.806{col 42}{txt}AIC used by Stata:{col 69}{res}    674.741
{txt}
{com}. 
{txt}end of do-file

{com}. do "C:\Users\mtr012\AppData\Local\Temp\STD00000000.tmp"
{txt}
{com}. *Table A13
. 
. logit punish gwf_personalist irr_entry max_purges  previous_death instit_control, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-480.65888}  
Iteration 1:{space 3}log pseudolikelihood = {res:-403.39276}  
Iteration 2:{space 3}log pseudolikelihood = {res:-399.86335}  
Iteration 3:{space 3}log pseudolikelihood = {res:-399.79447}  
Iteration 4:{space 3}log pseudolikelihood = {res:-399.79444}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       921
{txt}{col 49}Wald chi2({res}5{txt}){col 67}= {res}     75.26
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-399.79444{txt}{col 49}Pseudo R2{col 67}= {res}    0.1682

{txt}{ralign 81:(Std. Err. adjusted for {res:125} clusters in ccode)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}         punish{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
gwf_personalist {c |}{col 17}{res}{space 2} 1.871815{col 29}{space 2} .3809623{col 40}{space 1}    4.91{col 49}{space 3}0.000{col 57}{space 4} 1.125142{col 70}{space 3} 2.618487
{txt}{space 6}irr_entry {c |}{col 17}{res}{space 2} .5704701{col 29}{space 2} .2689465{col 40}{space 1}    2.12{col 49}{space 3}0.034{col 57}{space 4} .0433446{col 70}{space 3} 1.097596
{txt}{space 5}max_purges {c |}{col 17}{res}{space 2} .2776159{col 29}{space 2}  .193635{col 40}{space 1}    1.43{col 49}{space 3}0.152{col 57}{space 4}-.1019016{col 70}{space 3} .6571334
{txt}previous_deaths {c |}{col 17}{res}{space 2} .2669643{col 29}{space 2} .1144245{col 40}{space 1}    2.33{col 49}{space 3}0.020{col 57}{space 4} .0426964{col 70}{space 3} .4912321
{txt}{space 1}instit_control {c |}{col 17}{res}{space 2}-1.258207{col 29}{space 2} .3013662{col 40}{space 1}   -4.18{col 49}{space 3}0.000{col 57}{space 4}-1.848874{col 70}{space 3}-.6675402
{txt}{space 10}_cons {c |}{col 17}{res}{space 2}-.8667979{col 29}{space 2} .3344452{col 40}{space 1}   -2.59{col 49}{space 3}0.010{col 57}{space 4}-1.522299{col 70}{space 3}-.2112973
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a29
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}   -480.659{col 42}{txt}Log-Lik Full Model:{col 69}{res}   -399.794
{txt}D(915):{col 28}{res}    799.589{col 42}{txt}LR(5):{col 69}{res}    161.729
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.000
{txt}McFadden's R2:{col 28}{res}      0.168{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.156
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.161{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.249
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.242{col 42}{txt}Efron's R2:{col 69}{res}      0.207
{txt}Variance of y*:{col 28}{res}      4.338{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.813{col 42}{txt}Adj Count R2:{col 69}{res}      0.136
{txt}AIC:{col 28}{res}      0.881{col 42}{txt}AIC*n:{col 69}{res}    811.589
{txt}BIC:{col 28}{res}  -5445.707{col 42}{txt}BIC':{col 69}{res}   -127.602
{txt}BIC used by Stata:{col 28}{res}    840.542{col 42}{txt}AIC used by Stata:{col 69}{res}    811.589
{txt}
{com}. 
. logit punish max_person_scale max_military_scale gwf_democracy irr_entry max_purges previous_death instit_control, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-353.92557}  
Iteration 1:{space 3}log pseudolikelihood = {res:-249.21938}  
Iteration 2:{space 3}log pseudolikelihood = {res:-237.63312}  
Iteration 3:{space 3}log pseudolikelihood = {res: -237.0371}  
Iteration 4:{space 3}log pseudolikelihood = {res:-237.03581}  
Iteration 5:{space 3}log pseudolikelihood = {res:-237.03581}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       730
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}    149.91
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-237.03581{txt}{col 49}Pseudo R2{col 67}= {res}    0.3303

{txt}{ralign 84:(Std. Err. adjusted for {res:113} clusters in ccode)}
{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}            punish{col 20}{c |}      Coef.{col 32}   Std. Err.{col 44}      z{col 52}   P>|z|{col 60}     [95% Con{col 73}f. Interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}max_person_scale {c |}{col 20}{res}{space 2} 2.470774{col 32}{space 2} .4249225{col 43}{space 1}    5.81{col 52}{space 3}0.000{col 60}{space 4} 1.637941{col 73}{space 3} 3.303607
{txt}max_military_scale {c |}{col 20}{res}{space 2} 2.365555{col 32}{space 2} .3381365{col 43}{space 1}    7.00{col 52}{space 3}0.000{col 60}{space 4}  1.70282{col 73}{space 3} 3.028291
{txt}{space 5}gwf_democracy {c |}{col 20}{res}{space 2}-.5822401{col 32}{space 2} .3409162{col 43}{space 1}   -1.71{col 52}{space 3}0.088{col 60}{space 4}-1.250423{col 73}{space 3} .0859433
{txt}{space 9}irr_entry {c |}{col 20}{res}{space 2} -1.20592{col 32}{space 2} .4311708{col 43}{space 1}   -2.80{col 52}{space 3}0.005{col 60}{space 4}-2.050999{col 73}{space 3}-.3608409
{txt}{space 8}max_purges {c |}{col 20}{res}{space 2} .0341311{col 32}{space 2} .0561823{col 43}{space 1}    0.61{col 52}{space 3}0.544{col 60}{space 4}-.0759842{col 73}{space 3} .1442464
{txt}{space 3}previous_deaths {c |}{col 20}{res}{space 2} .3666987{col 32}{space 2} .1018393{col 43}{space 1}    3.60{col 52}{space 3}0.000{col 60}{space 4} .1670973{col 73}{space 3}    .5663
{txt}{space 4}instit_control {c |}{col 20}{res}{space 2}-.2681771{col 32}{space 2}  .331067{col 43}{space 1}   -0.81{col 52}{space 3}0.418{col 60}{space 4}-.9170566{col 73}{space 3} .3807024
{txt}{space 13}_cons {c |}{col 20}{res}{space 2}-2.232263{col 32}{space 2} .4367972{col 43}{space 1}   -5.11{col 52}{space 3}0.000{col 60}{space 4} -3.08837{col 73}{space 3}-1.376156
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a30
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}   -353.926{col 42}{txt}Log-Lik Full Model:{col 69}{res}   -237.036
{txt}D(722):{col 28}{res}    474.072{col 42}{txt}LR(7):{col 69}{res}    233.780
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.000
{txt}McFadden's R2:{col 28}{res}      0.330{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.308
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.274{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.441
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.396{col 42}{txt}Efron's R2:{col 69}{res}      0.356
{txt}Variance of y*:{col 28}{res}      5.448{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.863{col 42}{txt}Adj Count R2:{col 69}{res}      0.275
{txt}AIC:{col 28}{res}      0.671{col 42}{txt}AIC*n:{col 69}{res}    490.072
{txt}BIC:{col 28}{res}  -4286.107{col 42}{txt}BIC':{col 69}{res}   -187.628
{txt}BIC used by Stata:{col 28}{res}    526.816{col 42}{txt}AIC used by Stata:{col 69}{res}    490.072
{txt}
{com}. 
. logit punish max_pers_magaloni max_military_scale gwf_democracy irr_entry max_purges  previous_death instit_control, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-364.63795}  
Iteration 1:{space 3}log pseudolikelihood = {res:-249.24174}  
Iteration 2:{space 3}log pseudolikelihood = {res:-236.74706}  
Iteration 3:{space 3}log pseudolikelihood = {res:-236.04037}  
Iteration 4:{space 3}log pseudolikelihood = {res:-236.03857}  
Iteration 5:{space 3}log pseudolikelihood = {res:-236.03857}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       721
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}    160.72
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-236.03857{txt}{col 49}Pseudo R2{col 67}= {res}    0.3527

{txt}{ralign 84:(Std. Err. adjusted for {res:113} clusters in ccode)}
{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}            punish{col 20}{c |}      Coef.{col 32}   Std. Err.{col 44}      z{col 52}   P>|z|{col 60}     [95% Con{col 73}f. Interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}max_pers_magaloni {c |}{col 20}{res}{space 2} 1.755394{col 32}{space 2}  .244982{col 43}{space 1}    7.17{col 52}{space 3}0.000{col 60}{space 4} 1.275238{col 73}{space 3}  2.23555
{txt}max_military_scale {c |}{col 20}{res}{space 2} .6701637{col 32}{space 2} .4180408{col 43}{space 1}    1.60{col 52}{space 3}0.109{col 60}{space 4}-.1491812{col 73}{space 3} 1.489509
{txt}{space 5}gwf_democracy {c |}{col 20}{res}{space 2}-.0864309{col 32}{space 2} .3211722{col 43}{space 1}   -0.27{col 52}{space 3}0.788{col 60}{space 4}-.7159168{col 73}{space 3}  .543055
{txt}{space 9}irr_entry {c |}{col 20}{res}{space 2}-.7240737{col 32}{space 2} .3748199{col 43}{space 1}   -1.93{col 52}{space 3}0.053{col 60}{space 4}-1.458707{col 73}{space 3} .0105597
{txt}{space 8}max_purges {c |}{col 20}{res}{space 2} .0016558{col 32}{space 2} .0720315{col 43}{space 1}    0.02{col 52}{space 3}0.982{col 60}{space 4}-.1395233{col 73}{space 3} .1428349
{txt}{space 3}previous_deaths {c |}{col 20}{res}{space 2} .2405601{col 32}{space 2} .0975469{col 43}{space 1}    2.47{col 52}{space 3}0.014{col 60}{space 4} .0493716{col 73}{space 3} .4317486
{txt}{space 4}instit_control {c |}{col 20}{res}{space 2}-.2559677{col 32}{space 2} .3762582{col 43}{space 1}   -0.68{col 52}{space 3}0.496{col 60}{space 4}-.9934202{col 73}{space 3} .4814849
{txt}{space 13}_cons {c |}{col 20}{res}{space 2}-2.757097{col 32}{space 2} .4569605{col 43}{space 1}   -6.03{col 52}{space 3}0.000{col 60}{space 4}-3.652723{col 73}{space 3}-1.861471
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a31
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}   -364.638{col 42}{txt}Log-Lik Full Model:{col 69}{res}   -236.039
{txt}D(713):{col 28}{res}    472.077{col 42}{txt}LR(7):{col 69}{res}    257.199
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.000
{txt}McFadden's R2:{col 28}{res}      0.353{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.331
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.300{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.472
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.419{col 42}{txt}Efron's R2:{col 69}{res}      0.376
{txt}Variance of y*:{col 28}{res}      5.660{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.861{col 42}{txt}Adj Count R2:{col 69}{res}      0.320
{txt}AIC:{col 28}{res}      0.677{col 42}{txt}AIC*n:{col 69}{res}    488.077
{txt}BIC:{col 28}{res}  -4219.919{col 42}{txt}BIC':{col 69}{res}   -211.134
{txt}BIC used by Stata:{col 28}{res}    524.722{col 42}{txt}AIC used by Stata:{col 69}{res}    488.077
{txt}
{com}. 
{txt}end of do-file

{com}. do "C:\Users\mtr012\AppData\Local\Temp\STD00000000.tmp"
{txt}
{com}. 
. *Table A14
. 
. logit punish gwf_personalist irr_entry max_purges  previous_punish instit_control, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-302.30004}  
Iteration 1:{space 3}log pseudolikelihood = {res:-243.34264}  
Iteration 2:{space 3}log pseudolikelihood = {res:-239.10762}  
Iteration 3:{space 3}log pseudolikelihood = {res:-238.78184}  
Iteration 4:{space 3}log pseudolikelihood = {res:-238.78084}  
Iteration 5:{space 3}log pseudolikelihood = {res:-238.78084}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       637
{txt}{col 49}Wald chi2({res}5{txt}){col 67}= {res}     57.87
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-238.78084{txt}{col 49}Pseudo R2{col 67}= {res}    0.2101

{txt}{ralign 81:(Std. Err. adjusted for {res:109} clusters in ccode)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}         punish{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
gwf_personalist {c |}{col 17}{res}{space 2} 2.096535{col 29}{space 2} .4076005{col 40}{space 1}    5.14{col 49}{space 3}0.000{col 57}{space 4} 1.297653{col 70}{space 3} 2.895418
{txt}{space 6}irr_entry {c |}{col 17}{res}{space 2} .9351064{col 29}{space 2} .3404929{col 40}{space 1}    2.75{col 49}{space 3}0.006{col 57}{space 4} .2677526{col 70}{space 3}  1.60246
{txt}{space 5}max_purges {c |}{col 17}{res}{space 2} .1612638{col 29}{space 2} .2964896{col 40}{space 1}    0.54{col 49}{space 3}0.587{col 57}{space 4}-.4198452{col 70}{space 3} .7423728
{txt}previous_punish {c |}{col 17}{res}{space 2}-.6304115{col 29}{space 2} .3040886{col 40}{space 1}   -2.07{col 49}{space 3}0.038{col 57}{space 4}-1.226414{col 70}{space 3}-.0344087
{txt}{space 1}instit_control {c |}{col 17}{res}{space 2}-1.499748{col 29}{space 2} .3894253{col 40}{space 1}   -3.85{col 49}{space 3}0.000{col 57}{space 4}-2.263007{col 70}{space 3}-.7364881
{txt}{space 10}_cons {c |}{col 17}{res}{space 2} -.310845{col 29}{space 2} .4180466{col 40}{space 1}   -0.74{col 49}{space 3}0.457{col 57}{space 4}-1.130201{col 70}{space 3} .5085114
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a32
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}   -302.300{col 42}{txt}Log-Lik Full Model:{col 69}{res}   -238.781
{txt}D(631):{col 28}{res}    477.562{col 42}{txt}LR(5):{col 69}{res}    127.038
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.000
{txt}McFadden's R2:{col 28}{res}      0.210{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.190
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.181{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.295
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.272{col 42}{txt}Efron's R2:{col 69}{res}      0.251
{txt}Variance of y*:{col 28}{res}      4.516{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.856{col 42}{txt}Adj Count R2:{col 69}{res}      0.207
{txt}AIC:{col 28}{res}      0.769{col 42}{txt}AIC*n:{col 69}{res}    489.562
{txt}BIC:{col 28}{res}  -3596.660{col 42}{txt}BIC':{col 69}{res}    -94.755
{txt}BIC used by Stata:{col 28}{res}    516.302{col 42}{txt}AIC used by Stata:{col 69}{res}    489.562
{txt}
{com}. 
. logit punish max_person_scale max_military_scale gwf_democracy irr_entry max_purges previous_punish instit_control, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-227.08285}  
Iteration 1:{space 3}log pseudolikelihood = {res:-155.96272}  
Iteration 2:{space 3}log pseudolikelihood = {res:-144.98639}  
Iteration 3:{space 3}log pseudolikelihood = {res:-143.41404}  
Iteration 4:{space 3}log pseudolikelihood = {res:-143.41111}  
Iteration 5:{space 3}log pseudolikelihood = {res:-143.41111}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       504
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}     97.12
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-143.41111{txt}{col 49}Pseudo R2{col 67}= {res}    0.3685

{txt}{ralign 84:(Std. Err. adjusted for {res:96} clusters in ccode)}
{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}            punish{col 20}{c |}      Coef.{col 32}   Std. Err.{col 44}      z{col 52}   P>|z|{col 60}     [95% Con{col 73}f. Interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}max_person_scale {c |}{col 20}{res}{space 2} 2.344239{col 32}{space 2} .4244613{col 43}{space 1}    5.52{col 52}{space 3}0.000{col 60}{space 4}  1.51231{col 73}{space 3} 3.176167
{txt}max_military_scale {c |}{col 20}{res}{space 2} 2.256375{col 32}{space 2} .5014764{col 43}{space 1}    4.50{col 52}{space 3}0.000{col 60}{space 4}   1.2735{col 73}{space 3} 3.239251
{txt}{space 5}gwf_democracy {c |}{col 20}{res}{space 2}-.7399186{col 32}{space 2}  .387877{col 43}{space 1}   -1.91{col 52}{space 3}0.056{col 60}{space 4}-1.500144{col 73}{space 3} .0203064
{txt}{space 9}irr_entry {c |}{col 20}{res}{space 2}-.9298272{col 32}{space 2} .4951307{col 43}{space 1}   -1.88{col 52}{space 3}0.060{col 60}{space 4}-1.900266{col 73}{space 3} .0406112
{txt}{space 8}max_purges {c |}{col 20}{res}{space 2} .0293254{col 32}{space 2}   .04749{col 43}{space 1}    0.62{col 52}{space 3}0.537{col 60}{space 4}-.0637532{col 73}{space 3}  .122404
{txt}{space 3}previous_punish {c |}{col 20}{res}{space 2}-.7090355{col 32}{space 2} .3353786{col 43}{space 1}   -2.11{col 52}{space 3}0.035{col 60}{space 4}-1.366366{col 73}{space 3}-.0517054
{txt}{space 4}instit_control {c |}{col 20}{res}{space 2}-.4828629{col 32}{space 2} .5242737{col 43}{space 1}   -0.92{col 52}{space 3}0.357{col 60}{space 4}-1.510421{col 73}{space 3} .5446948
{txt}{space 13}_cons {c |}{col 20}{res}{space 2}  -1.4067{col 32}{space 2} .6217652{col 43}{space 1}   -2.26{col 52}{space 3}0.024{col 60}{space 4}-2.625337{col 73}{space 3}-.1880624
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a33
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}   -227.083{col 42}{txt}Log-Lik Full Model:{col 69}{res}   -143.411
{txt}D(496):{col 28}{res}    286.822{col 42}{txt}LR(7):{col 69}{res}    167.343
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.000
{txt}McFadden's R2:{col 28}{res}      0.368{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.333
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.283{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.476
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.421{col 42}{txt}Efron's R2:{col 69}{res}      0.391
{txt}Variance of y*:{col 28}{res}      5.678{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.899{col 42}{txt}Adj Count R2:{col 69}{res}      0.393
{txt}AIC:{col 28}{res}      0.601{col 42}{txt}AIC*n:{col 69}{res}    302.822
{txt}BIC:{col 28}{res}  -2799.576{col 42}{txt}BIC':{col 69}{res}   -123.785
{txt}BIC used by Stata:{col 28}{res}    336.603{col 42}{txt}AIC used by Stata:{col 69}{res}    302.822
{txt}
{com}. 
. logit punish max_pers_magaloni max_military_scale gwf_democracy irr_entry max_purges  previous_punish instit_control, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -232.0541}  
Iteration 1:{space 3}log pseudolikelihood = {res:-151.37785}  
Iteration 2:{space 3}log pseudolikelihood = {res:-138.56765}  
Iteration 3:{space 3}log pseudolikelihood = {res:-136.44334}  
Iteration 4:{space 3}log pseudolikelihood = {res:-136.43708}  
Iteration 5:{space 3}log pseudolikelihood = {res:-136.43708}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       497
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}    129.81
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-136.43708{txt}{col 49}Pseudo R2{col 67}= {res}    0.4120

{txt}{ralign 84:(Std. Err. adjusted for {res:96} clusters in ccode)}
{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}            punish{col 20}{c |}      Coef.{col 32}   Std. Err.{col 44}      z{col 52}   P>|z|{col 60}     [95% Con{col 73}f. Interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}max_pers_magaloni {c |}{col 20}{res}{space 2} 1.714007{col 32}{space 2} .2833972{col 43}{space 1}    6.05{col 52}{space 3}0.000{col 60}{space 4} 1.158558{col 73}{space 3} 2.269455
{txt}max_military_scale {c |}{col 20}{res}{space 2} 1.030922{col 32}{space 2} .5726751{col 43}{space 1}    1.80{col 52}{space 3}0.072{col 60}{space 4}-.0915006{col 73}{space 3} 2.153345
{txt}{space 5}gwf_democracy {c |}{col 20}{res}{space 2}-.2132023{col 32}{space 2} .4027141{col 43}{space 1}   -0.53{col 52}{space 3}0.597{col 60}{space 4}-1.002507{col 73}{space 3} .5761028
{txt}{space 9}irr_entry {c |}{col 20}{res}{space 2}-.2318809{col 32}{space 2} .4902444{col 43}{space 1}   -0.47{col 52}{space 3}0.636{col 60}{space 4}-1.192742{col 73}{space 3} .7289805
{txt}{space 8}max_purges {c |}{col 20}{res}{space 2}-.0250462{col 32}{space 2} .0538689{col 43}{space 1}   -0.46{col 52}{space 3}0.642{col 60}{space 4}-.1306272{col 73}{space 3} .0805348
{txt}{space 3}previous_punish {c |}{col 20}{res}{space 2}-.8902613{col 32}{space 2} .3441222{col 43}{space 1}   -2.59{col 52}{space 3}0.010{col 60}{space 4}-1.564728{col 73}{space 3}-.2157942
{txt}{space 4}instit_control {c |}{col 20}{res}{space 2}-.2824504{col 32}{space 2} .5457827{col 43}{space 1}   -0.52{col 52}{space 3}0.605{col 60}{space 4}-1.352165{col 73}{space 3} .7872641
{txt}{space 13}_cons {c |}{col 20}{res}{space 2}-2.245323{col 32}{space 2} .6566816{col 43}{space 1}   -3.42{col 52}{space 3}0.001{col 60}{space 4}-3.532395{col 73}{space 3}-.9582505
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a34
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}   -232.054{col 42}{txt}Log-Lik Full Model:{col 69}{res}   -136.437
{txt}D(489):{col 28}{res}    272.874{col 42}{txt}LR(7):{col 69}{res}    191.234
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.000
{txt}McFadden's R2:{col 28}{res}      0.412{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.378
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.319{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.526
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.470{col 42}{txt}Efron's R2:{col 69}{res}      0.437
{txt}Variance of y*:{col 28}{res}      6.203{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.891{col 42}{txt}Adj Count R2:{col 69}{res}      0.386
{txt}AIC:{col 28}{res}      0.581{col 42}{txt}AIC*n:{col 69}{res}    288.874
{txt}BIC:{col 28}{res}  -2763.126{col 42}{txt}BIC':{col 69}{res}   -147.774
{txt}BIC used by Stata:{col 28}{res}    322.543{col 42}{txt}AIC used by Stata:{col 69}{res}    288.874
{txt}
{com}. 
{txt}end of do-file

{com}. do "C:\Users\mtr012\AppData\Local\Temp\STD00000000.tmp"
{txt}
{com}. *Table A15
. 
. logit punish gwf_personalist irr_entry max_purges  death_entry instit_control, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-399.15675}  
Iteration 1:{space 3}log pseudolikelihood = {res:-339.58142}  
Iteration 2:{space 3}log pseudolikelihood = {res:-334.69755}  
Iteration 3:{space 3}log pseudolikelihood = {res:-334.47123}  
Iteration 4:{space 3}log pseudolikelihood = {res:-334.47097}  
Iteration 5:{space 3}log pseudolikelihood = {res:-334.47097}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       814
{txt}{col 49}Wald chi2({res}5{txt}){col 67}= {res}     73.33
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-334.47097{txt}{col 49}Pseudo R2{col 67}= {res}    0.1621

{txt}{ralign 81:(Std. Err. adjusted for {res:118} clusters in ccode)}
{hline 16}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 17}{c |}{col 29}    Robust
{col 1}         punish{col 17}{c |}      Coef.{col 29}   Std. Err.{col 41}      z{col 49}   P>|z|{col 57}     [95% Con{col 70}f. Interval]
{hline 16}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
gwf_personalist {c |}{col 17}{res}{space 2} 1.622996{col 29}{space 2} .3787716{col 40}{space 1}    4.28{col 49}{space 3}0.000{col 57}{space 4} .8806174{col 70}{space 3} 2.365375
{txt}{space 6}irr_entry {c |}{col 17}{res}{space 2} .8850497{col 29}{space 2}  .296593{col 40}{space 1}    2.98{col 49}{space 3}0.003{col 57}{space 4} .3037382{col 70}{space 3} 1.466361
{txt}{space 5}max_purges {c |}{col 17}{res}{space 2} .2683642{col 29}{space 2} .2032729{col 40}{space 1}    1.32{col 49}{space 3}0.187{col 57}{space 4}-.1300434{col 70}{space 3} .6667718
{txt}{space 4}death_entry {c |}{col 17}{res}{space 2} .6644397{col 29}{space 2}  .482005{col 40}{space 1}    1.38{col 49}{space 3}0.168{col 57}{space 4}-.2802728{col 70}{space 3} 1.609152
{txt}{space 1}instit_control {c |}{col 17}{res}{space 2}-.9575832{col 29}{space 2} .2993534{col 40}{space 1}   -3.20{col 49}{space 3}0.001{col 57}{space 4}-1.544305{col 70}{space 3}-.3708614
{txt}{space 10}_cons {c |}{col 17}{res}{space 2}-1.144632{col 29}{space 2} .3253016{col 40}{space 1}   -3.52{col 49}{space 3}0.000{col 57}{space 4}-1.782211{col 70}{space 3}-.5070522
{txt}{hline 16}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a35
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}   -399.157{col 42}{txt}Log-Lik Full Model:{col 69}{res}   -334.471
{txt}D(808):{col 28}{res}    668.942{col 42}{txt}LR(5):{col 69}{res}    129.372
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.000
{txt}McFadden's R2:{col 28}{res}      0.162{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.147
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.147{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.235
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.222{col 42}{txt}Efron's R2:{col 69}{res}      0.204
{txt}Variance of y*:{col 28}{res}      4.230{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.834{col 42}{txt}Adj Count R2:{col 69}{res}      0.140
{txt}AIC:{col 28}{res}      0.837{col 42}{txt}AIC*n:{col 69}{res}    680.942
{txt}BIC:{col 28}{res}  -4746.242{col 42}{txt}BIC':{col 69}{res}    -95.862
{txt}BIC used by Stata:{col 28}{res}    709.154{col 42}{txt}AIC used by Stata:{col 69}{res}    680.942
{txt}
{com}. 
. logit punish max_person_scale max_military_scale gwf_democracy irr_entry max_purges death_entry instit_control, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-301.77376}  
Iteration 1:{space 3}log pseudolikelihood = {res:-224.60263}  
Iteration 2:{space 3}log pseudolikelihood = {res:-212.82297}  
Iteration 3:{space 3}log pseudolikelihood = {res:-211.87204}  
Iteration 4:{space 3}log pseudolikelihood = {res:-211.86963}  
Iteration 5:{space 3}log pseudolikelihood = {res:-211.86963}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       658
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}    110.94
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-211.86963{txt}{col 49}Pseudo R2{col 67}= {res}    0.2979

{txt}{ralign 84:(Std. Err. adjusted for {res:106} clusters in ccode)}
{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}            punish{col 20}{c |}      Coef.{col 32}   Std. Err.{col 44}      z{col 52}   P>|z|{col 60}     [95% Con{col 73}f. Interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}max_person_scale {c |}{col 20}{res}{space 2} 2.122071{col 32}{space 2} .3789104{col 43}{space 1}    5.60{col 52}{space 3}0.000{col 60}{space 4}  1.37942{col 73}{space 3} 2.864721
{txt}max_military_scale {c |}{col 20}{res}{space 2} 2.503115{col 32}{space 2} .3875868{col 43}{space 1}    6.46{col 52}{space 3}0.000{col 60}{space 4} 1.743459{col 73}{space 3} 3.262771
{txt}{space 5}gwf_democracy {c |}{col 20}{res}{space 2}-.1847437{col 32}{space 2} .3669875{col 43}{space 1}   -0.50{col 52}{space 3}0.615{col 60}{space 4} -.904026{col 73}{space 3} .5345386
{txt}{space 9}irr_entry {c |}{col 20}{res}{space 2}-.7927564{col 32}{space 2} .4658253{col 43}{space 1}   -1.70{col 52}{space 3}0.089{col 60}{space 4}-1.705757{col 73}{space 3} .1202445
{txt}{space 8}max_purges {c |}{col 20}{res}{space 2} .0388679{col 32}{space 2} .0631676{col 43}{space 1}    0.62{col 52}{space 3}0.538{col 60}{space 4}-.0849383{col 73}{space 3} .1626741
{txt}{space 7}death_entry {c |}{col 20}{res}{space 2} .5497162{col 32}{space 2} .5131769{col 43}{space 1}    1.07{col 52}{space 3}0.284{col 60}{space 4} -.456092{col 73}{space 3} 1.555524
{txt}{space 4}instit_control {c |}{col 20}{res}{space 2}-.1995583{col 32}{space 2} .3934171{col 43}{space 1}   -0.51{col 52}{space 3}0.612{col 60}{space 4}-.9706417{col 73}{space 3}  .571525
{txt}{space 13}_cons {c |}{col 20}{res}{space 2}-2.443242{col 32}{space 2} .4949586{col 43}{space 1}   -4.94{col 52}{space 3}0.000{col 60}{space 4}-3.413343{col 73}{space 3}-1.473141
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a36
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}   -301.774{col 42}{txt}Log-Lik Full Model:{col 69}{res}   -211.870
{txt}D(650):{col 28}{res}    423.739{col 42}{txt}LR(7):{col 69}{res}    179.808
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.000
{txt}McFadden's R2:{col 28}{res}      0.298{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.271
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.239{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.398
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.345{col 42}{txt}Efron's R2:{col 69}{res}      0.325
{txt}Variance of y*:{col 28}{res}      5.021{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.872{col 42}{txt}Adj Count R2:{col 69}{res}      0.257
{txt}AIC:{col 28}{res}      0.668{col 42}{txt}AIC*n:{col 69}{res}    439.739
{txt}BIC:{col 28}{res}  -3794.244{col 42}{txt}BIC':{col 69}{res}   -134.384
{txt}BIC used by Stata:{col 28}{res}    475.653{col 42}{txt}AIC used by Stata:{col 69}{res}    439.739
{txt}
{com}. 
. logit punish max_pers_magaloni max_military_scale gwf_democracy irr_entry max_purges  death_entry instit_control, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-305.40927}  
Iteration 1:{space 3}log pseudolikelihood = {res:-222.01421}  
Iteration 2:{space 3}log pseudolikelihood = {res:-209.98026}  
Iteration 3:{space 3}log pseudolikelihood = {res:-208.76277}  
Iteration 4:{space 3}log pseudolikelihood = {res:-208.75934}  
Iteration 5:{space 3}log pseudolikelihood = {res:-208.75934}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       645
{txt}{col 49}Wald chi2({res}7{txt}){col 67}= {res}    113.60
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-208.75934{txt}{col 49}Pseudo R2{col 67}= {res}    0.3165

{txt}{ralign 84:(Std. Err. adjusted for {res:106} clusters in ccode)}
{hline 19}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 20}{c |}{col 32}    Robust
{col 1}            punish{col 20}{c |}      Coef.{col 32}   Std. Err.{col 44}      z{col 52}   P>|z|{col 60}     [95% Con{col 73}f. Interval]
{hline 19}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 1}max_pers_magaloni {c |}{col 20}{res}{space 2} 1.617419{col 32}{space 2} .2935225{col 43}{space 1}    5.51{col 52}{space 3}0.000{col 60}{space 4} 1.042125{col 73}{space 3} 2.192712
{txt}max_military_scale {c |}{col 20}{res}{space 2} .7335007{col 32}{space 2}  .502364{col 43}{space 1}    1.46{col 52}{space 3}0.144{col 60}{space 4}-.2511147{col 73}{space 3} 1.718116
{txt}{space 5}gwf_democracy {c |}{col 20}{res}{space 2} .3060424{col 32}{space 2} .3226728{col 43}{space 1}    0.95{col 52}{space 3}0.343{col 60}{space 4}-.3263847{col 73}{space 3} .9384695
{txt}{space 9}irr_entry {c |}{col 20}{res}{space 2}-.4609703{col 32}{space 2} .3957251{col 43}{space 1}   -1.16{col 52}{space 3}0.244{col 60}{space 4}-1.236577{col 73}{space 3} .3146366
{txt}{space 8}max_purges {c |}{col 20}{res}{space 2}  .020239{col 32}{space 2} .0820284{col 43}{space 1}    0.25{col 52}{space 3}0.805{col 60}{space 4}-.1405337{col 73}{space 3} .1810118
{txt}{space 7}death_entry {c |}{col 20}{res}{space 2} 1.122357{col 32}{space 2}   .51635{col 43}{space 1}    2.17{col 52}{space 3}0.030{col 60}{space 4} .1103297{col 73}{space 3} 2.134385
{txt}{space 4}instit_control {c |}{col 20}{res}{space 2}-.3587027{col 32}{space 2} .4091786{col 43}{space 1}   -0.88{col 52}{space 3}0.381{col 60}{space 4}-1.160678{col 73}{space 3} .4432726
{txt}{space 13}_cons {c |}{col 20}{res}{space 2}-2.839418{col 32}{space 2} .4511082{col 43}{space 1}   -6.29{col 52}{space 3}0.000{col 60}{space 4}-3.723574{col 73}{space 3}-1.955263
{txt}{hline 19}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a37
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}   -305.409{col 42}{txt}Log-Lik Full Model:{col 69}{res}   -208.759
{txt}D(637):{col 28}{res}    417.519{col 42}{txt}LR(7):{col 69}{res}    193.300
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.000
{txt}McFadden's R2:{col 28}{res}      0.316{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.290
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.259{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.423
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.366{col 42}{txt}Efron's R2:{col 69}{res}      0.336
{txt}Variance of y*:{col 28}{res}      5.189{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.862{col 42}{txt}Adj Count R2:{col 69}{res}      0.239
{txt}AIC:{col 28}{res}      0.672{col 42}{txt}AIC*n:{col 69}{res}    433.519
{txt}BIC:{col 28}{res}  -3703.394{col 42}{txt}BIC':{col 69}{res}   -148.015
{txt}BIC used by Stata:{col 28}{res}    469.273{col 42}{txt}AIC used by Stata:{col 69}{res}    433.519
{txt}
{com}. 
{txt}end of do-file

{com}. do "C:\Users\mtr012\AppData\Local\Temp\STD00000000.tmp"
{txt}
{com}. *Table A16
. 
. logit punish max_pers_magaloni max_military_scale gwf_democracy irr_entry max_purges previous_sum_punish instit_control outyear, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-364.63795}  
Iteration 1:{space 3}log pseudolikelihood = {res:-250.65873}  
Iteration 2:{space 3}log pseudolikelihood = {res:-238.49944}  
Iteration 3:{space 3}log pseudolikelihood = {res:-237.79473}  
Iteration 4:{space 3}log pseudolikelihood = {res:-237.79303}  
Iteration 5:{space 3}log pseudolikelihood = {res:-237.79303}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       721
{txt}{col 49}Wald chi2({res}8{txt}){col 67}= {res}    146.76
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-237.79303{txt}{col 49}Pseudo R2{col 67}= {res}    0.3479

{txt}{ralign 85:(Std. Err. adjusted for {res:113} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}             punish{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}max_pers_magaloni {c |}{col 21}{res}{space 2} 1.783034{col 33}{space 2} .2474381{col 44}{space 1}    7.21{col 53}{space 3}0.000{col 61}{space 4} 1.298064{col 74}{space 3} 2.268004
{txt}{space 1}max_military_scale {c |}{col 21}{res}{space 2} .5602635{col 33}{space 2} .4489484{col 44}{space 1}    1.25{col 53}{space 3}0.212{col 61}{space 4}-.3196593{col 74}{space 3} 1.440186
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2}-.0285601{col 33}{space 2} .3307623{col 44}{space 1}   -0.09{col 53}{space 3}0.931{col 61}{space 4}-.6768423{col 74}{space 3}  .619722
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2}-.6601896{col 33}{space 2}   .38249{col 44}{space 1}   -1.73{col 53}{space 3}0.084{col 61}{space 4}-1.409856{col 74}{space 3}  .089477
{txt}{space 9}max_purges {c |}{col 21}{res}{space 2} .0056405{col 33}{space 2} .0765673{col 44}{space 1}    0.07{col 53}{space 3}0.941{col 61}{space 4}-.1444286{col 74}{space 3} .1557097
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0184149{col 33}{space 2} .0163911{col 44}{space 1}    1.12{col 53}{space 3}0.261{col 61}{space 4}-.0137111{col 74}{space 3} .0505409
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.2927799{col 33}{space 2} .4034219{col 44}{space 1}   -0.73{col 53}{space 3}0.468{col 61}{space 4}-1.083472{col 74}{space 3} .4979124
{txt}{space 12}outyear {c |}{col 21}{res}{space 2} -.000758{col 33}{space 2} .0084853{col 44}{space 1}   -0.09{col 53}{space 3}0.929{col 61}{space 4}-.0173889{col 74}{space 3} .0158729
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-1.167714{col 33}{space 2} 16.71619{col 44}{space 1}   -0.07{col 53}{space 3}0.944{col 61}{space 4}-33.93085{col 74}{space 3} 31.59542
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a38
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}   -364.638{col 42}{txt}Log-Lik Full Model:{col 69}{res}   -237.793
{txt}D(712):{col 28}{res}    475.586{col 42}{txt}LR(8):{col 69}{res}    253.690
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.000
{txt}McFadden's R2:{col 28}{res}      0.348{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.323
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.297{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.466
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.410{col 42}{txt}Efron's R2:{col 69}{res}      0.375
{txt}Variance of y*:{col 28}{res}      5.576{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.861{col 42}{txt}Adj Count R2:{col 69}{res}      0.320
{txt}AIC:{col 28}{res}      0.685{col 42}{txt}AIC*n:{col 69}{res}    493.586
{txt}BIC:{col 28}{res}  -4209.829{col 42}{txt}BIC':{col 69}{res}   -201.045
{txt}BIC used by Stata:{col 28}{res}    534.812{col 42}{txt}AIC used by Stata:{col 69}{res}    493.586
{txt}
{com}. 
. logit punish max_pers_magaloni max_military_scale gwf_democracy irr_entry max_purges  previous_sum_punish instit_control duration, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-364.63795}  
Iteration 1:{space 3}log pseudolikelihood = {res:-248.90084}  
Iteration 2:{space 3}log pseudolikelihood = {res:-236.46237}  
Iteration 3:{space 3}log pseudolikelihood = {res:-235.77504}  
Iteration 4:{space 3}log pseudolikelihood = {res:-235.77334}  
Iteration 5:{space 3}log pseudolikelihood = {res:-235.77334}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       721
{txt}{col 49}Wald chi2({res}8{txt}){col 67}= {res}    153.22
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-235.77334{txt}{col 49}Pseudo R2{col 67}= {res}    0.3534

{txt}{ralign 85:(Std. Err. adjusted for {res:113} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}             punish{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}max_pers_magaloni {c |}{col 21}{res}{space 2}  1.71939{col 33}{space 2} .2548972{col 44}{space 1}    6.75{col 53}{space 3}0.000{col 61}{space 4} 1.219801{col 74}{space 3}  2.21898
{txt}{space 1}max_military_scale {c |}{col 21}{res}{space 2} .7527638{col 33}{space 2} .4453344{col 44}{space 1}    1.69{col 53}{space 3}0.091{col 61}{space 4}-.1200756{col 74}{space 3} 1.625603
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2} .2625838{col 33}{space 2} .3557829{col 44}{space 1}    0.74{col 53}{space 3}0.460{col 61}{space 4} -.434738{col 74}{space 3} .9599055
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2}-.6330215{col 33}{space 2} .3734304{col 44}{space 1}   -1.70{col 53}{space 3}0.090{col 61}{space 4}-1.364932{col 74}{space 3} .0988886
{txt}{space 9}max_purges {c |}{col 21}{res}{space 2} .0031547{col 33}{space 2} .0678498{col 44}{space 1}    0.05{col 53}{space 3}0.963{col 61}{space 4}-.1298286{col 74}{space 3} .1361379
{txt}previous_sum_punish {c |}{col 21}{res}{space 2}  .027859{col 33}{space 2}  .016881{col 44}{space 1}    1.65{col 53}{space 3}0.099{col 61}{space 4}-.0052272{col 74}{space 3} .0609453
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.3750867{col 33}{space 2} .3919024{col 44}{space 1}   -0.96{col 53}{space 3}0.339{col 61}{space 4}-1.143201{col 74}{space 3} .3930279
{txt}{space 11}duration {c |}{col 21}{res}{space 2} .0410742{col 33}{space 2} .0219723{col 44}{space 1}    1.87{col 53}{space 3}0.062{col 61}{space 4}-.0019908{col 74}{space 3} .0841391
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-3.117612{col 33}{space 2}  .496463{col 44}{space 1}   -6.28{col 53}{space 3}0.000{col 61}{space 4}-4.090661{col 74}{space 3}-2.144562
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a39
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}   -364.638{col 42}{txt}Log-Lik Full Model:{col 69}{res}   -235.773
{txt}D(712):{col 28}{res}    471.547{col 42}{txt}LR(8):{col 69}{res}    257.729
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.000
{txt}McFadden's R2:{col 28}{res}      0.353{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.329
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.301{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.472
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.418{col 42}{txt}Efron's R2:{col 69}{res}      0.382
{txt}Variance of y*:{col 28}{res}      5.651{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.863{col 42}{txt}Adj Count R2:{col 69}{res}      0.327
{txt}AIC:{col 28}{res}      0.679{col 42}{txt}AIC*n:{col 69}{res}    489.547
{txt}BIC:{col 28}{res}  -4213.868{col 42}{txt}BIC':{col 69}{res}   -205.084
{txt}BIC used by Stata:{col 28}{res}    530.772{col 42}{txt}AIC used by Stata:{col 69}{res}    489.547
{txt}
{com}. 
. logit punish max_pers_magaloni max_military_scale gwf_democracy irr_entry max_purges  previous_sum_punish instit_control punish_years, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-364.63795}  
Iteration 1:{space 3}log pseudolikelihood = {res:-250.07245}  
Iteration 2:{space 3}log pseudolikelihood = {res:-237.23126}  
Iteration 3:{space 3}log pseudolikelihood = {res:-236.37742}  
Iteration 4:{space 3}log pseudolikelihood = {res:-236.37463}  
Iteration 5:{space 3}log pseudolikelihood = {res:-236.37463}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       721
{txt}{col 49}Wald chi2({res}8{txt}){col 67}= {res}    143.27
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-236.37463{txt}{col 49}Pseudo R2{col 67}= {res}    0.3518

{txt}{ralign 85:(Std. Err. adjusted for {res:113} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}             punish{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}max_pers_magaloni {c |}{col 21}{res}{space 2} 1.738857{col 33}{space 2} .2433028{col 44}{space 1}    7.15{col 53}{space 3}0.000{col 61}{space 4} 1.261992{col 74}{space 3} 2.215722
{txt}{space 1}max_military_scale {c |}{col 21}{res}{space 2}  .481965{col 33}{space 2} .4273105{col 44}{space 1}    1.13{col 53}{space 3}0.259{col 61}{space 4}-.3555481{col 74}{space 3} 1.319478
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2}-.0701654{col 33}{space 2} .3310297{col 44}{space 1}   -0.21{col 53}{space 3}0.832{col 61}{space 4}-.7189717{col 74}{space 3} .5786409
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2}-.7178264{col 33}{space 2} .3701289{col 44}{space 1}   -1.94{col 53}{space 3}0.052{col 61}{space 4}-1.443266{col 74}{space 3} .0076128
{txt}{space 9}max_purges {c |}{col 21}{res}{space 2} .0021653{col 33}{space 2} .0698941{col 44}{space 1}    0.03{col 53}{space 3}0.975{col 61}{space 4}-.1348246{col 74}{space 3} .1391551
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0104149{col 33}{space 2} .0172571{col 44}{space 1}    0.60{col 53}{space 3}0.546{col 61}{space 4}-.0234084{col 74}{space 3} .0442381
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.2435155{col 33}{space 2} .3900998{col 44}{space 1}   -0.62{col 53}{space 3}0.532{col 61}{space 4}-1.008097{col 74}{space 3} .5210661
{txt}{space 7}punish_years {c |}{col 21}{res}{space 2}-.0101833{col 33}{space 2} .0062677{col 44}{space 1}   -1.62{col 53}{space 3}0.104{col 61}{space 4}-.0224677{col 74}{space 3} .0021012
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-2.350727{col 33}{space 2} .4899988{col 44}{space 1}   -4.80{col 53}{space 3}0.000{col 61}{space 4}-3.311107{col 74}{space 3}-1.390347
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a40
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}   -364.638{col 42}{txt}Log-Lik Full Model:{col 69}{res}   -236.375
{txt}D(712):{col 28}{res}    472.749{col 42}{txt}LR(8):{col 69}{res}    256.527
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.000
{txt}McFadden's R2:{col 28}{res}      0.352{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.327
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.299{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.470
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.426{col 42}{txt}Efron's R2:{col 69}{res}      0.376
{txt}Variance of y*:{col 28}{res}      5.731{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.860{col 42}{txt}Adj Count R2:{col 69}{res}      0.313
{txt}AIC:{col 28}{res}      0.681{col 42}{txt}AIC*n:{col 69}{res}    490.749
{txt}BIC:{col 28}{res}  -4212.666{col 42}{txt}BIC':{col 69}{res}   -203.882
{txt}BIC used by Stata:{col 28}{res}    531.975{col 42}{txt}AIC used by Stata:{col 69}{res}    490.749
{txt}
{com}. 
{txt}end of do-file

{com}. do "C:\Users\mtr012\AppData\Local\Temp\STD00000000.tmp"
{txt}
{com}. *Table A17
. logit punish max_person_scale max_military_scale gwf_democracy irr_entry max_purges previous_sum_punish instit_control i.ccode, cluster(ccode)

{txt}note: 2.ccode != 0 predicts failure perfectly
      2.ccode dropped and 9 obs not used

note: 20.ccode != 0 predicts failure perfectly
      20.ccode dropped and 10 obs not used

note: 91.ccode != 0 predicts failure perfectly
      91.ccode dropped and 7 obs not used

note: 94.ccode != 0 predicts failure perfectly
      94.ccode dropped and 13 obs not used

note: 100.ccode != 0 predicts failure perfectly
      100.ccode dropped and 11 obs not used

note: 165.ccode != 0 predicts failure perfectly
      165.ccode dropped and 18 obs not used

note: 200.ccode != 0 predicts failure perfectly
      200.ccode dropped and 10 obs not used

note: 205.ccode != 0 predicts failure perfectly
      205.ccode dropped and 15 obs not used

note: 210.ccode != 0 predicts failure perfectly
      210.ccode dropped and 14 obs not used

note: 211.ccode != 0 predicts failure perfectly
      211.ccode dropped and 22 obs not used

note: 220.ccode != 0 predicts failure perfectly
      220.ccode dropped and 27 obs not used

note: 225.ccode != 0 predicts failure perfectly
      225.ccode dropped and 13 obs not used

note: 230.ccode != 0 predicts failure perfectly
      230.ccode dropped and 4 obs not used

note: 260.ccode != 0 predicts failure perfectly
      260.ccode dropped and 6 obs not used

note: 290.ccode != 0 predicts failure perfectly
      290.ccode dropped and 3 obs not used

note: 305.ccode != 0 predicts failure perfectly
      305.ccode dropped and 7 obs not used

note: 310.ccode != 0 predicts failure perfectly
      310.ccode dropped and 5 obs not used

note: 315.ccode != 0 predicts failure perfectly
      315.ccode dropped and 4 obs not used

note: 317.ccode != 0 predicts failure perfectly
      317.ccode dropped and 3 obs not used

note: 325.ccode != 0 predicts failure perfectly
      325.ccode dropped and 34 obs not used

note: 343.ccode != 0 predicts failure perfectly
      343.ccode dropped and 2 obs not used

note: 349.ccode != 0 predicts failure perfectly
      349.ccode dropped and 2 obs not used

note: 359.ccode != 0 predicts failure perfectly
      359.ccode dropped and 1 obs not used

note: 365.ccode != 0 predicts failure perfectly
      365.ccode dropped and 2 obs not used

note: 366.ccode != 0 predicts failure perfectly
      366.ccode dropped and 7 obs not used

note: 367.ccode != 0 predicts failure perfectly
      367.ccode dropped and 7 obs not used

note: 368.ccode != 0 predicts failure perfectly
      368.ccode dropped and 3 obs not used

note: 369.ccode != 0 predicts failure perfectly
      369.ccode dropped and 1 obs not used

note: 375.ccode != 0 predicts failure perfectly
      375.ccode dropped and 3 obs not used

note: 385.ccode != 0 predicts failure perfectly
      385.ccode dropped and 18 obs not used

note: 404.ccode != 0 predicts success perfectly
      404.ccode dropped and 1 obs not used

note: 420.ccode != 0 predicts success perfectly
      420.ccode dropped and 1 obs not used

note: 433.ccode != 0 predicts failure perfectly
      433.ccode dropped and 2 obs not used

note: 434.ccode != 0 predicts failure perfectly
      434.ccode dropped and 2 obs not used

note: 437.ccode != 0 predicts success perfectly
      437.ccode dropped and 2 obs not used

note: 450.ccode != 0 predicts success perfectly
      450.ccode dropped and 2 obs not used

note: 451.ccode != 0 predicts success perfectly
      451.ccode dropped and 3 obs not used

note: 461.ccode != 0 predicts success perfectly
      461.ccode dropped and 1 obs not used

note: 481.ccode != 0 predicts success perfectly
      481.ccode dropped and 1 obs not used

note: 483.ccode != 0 predicts success perfectly
      483.ccode dropped and 1 obs not used

note: 500.ccode != 0 predicts success perfectly
      500.ccode dropped and 2 obs not used

note: 501.ccode != 0 predicts failure perfectly
      501.ccode dropped and 1 obs not used

note: 510.ccode != 0 predicts failure perfectly
      510.ccode dropped and 2 obs not used

note: 517.ccode != 0 predicts success perfectly
      517.ccode dropped and 1 obs not used

note: 530.ccode != 0 predicts success perfectly
      530.ccode dropped and 1 obs not used

note: 551.ccode != 0 predicts failure perfectly
      551.ccode dropped and 1 obs not used

note: 560.ccode != 0 predicts failure perfectly
      560.ccode dropped and 2 obs not used

note: 590.ccode != 0 predicts failure perfectly
      590.ccode dropped and 3 obs not used

note: 615.ccode != 0 predicts failure perfectly
      615.ccode dropped and 2 obs not used

note: 616.ccode != 0 predicts success perfectly
      616.ccode dropped and 1 obs not used

note: 620.ccode != 0 predicts success perfectly
      620.ccode dropped and 1 obs not used

note: 645.ccode != 0 predicts success perfectly
      645.ccode dropped and 2 obs not used

note: 651.ccode != 0 predicts success perfectly
      651.ccode dropped and 2 obs not used

note: 700.ccode != 0 predicts success perfectly
      700.ccode dropped and 1 obs not used

note: 712.ccode != 0 predicts failure perfectly
      712.ccode dropped and 1 obs not used

note: 790.ccode != 0 predicts failure perfectly
      790.ccode dropped and 2 obs not used

note: 816.ccode != 0 predicts failure perfectly
      816.ccode dropped and 3 obs not used

note: 817.ccode != 0 predicts success perfectly
      817.ccode dropped and 1 obs not used

note: 820.ccode != 0 predicts failure perfectly
      820.ccode dropped and 2 obs not used

note: 830.ccode != 0 predicts failure perfectly
      830.ccode dropped and 1 obs not used

note: 900.ccode != 0 predicts failure perfectly
      900.ccode dropped and 9 obs not used

note: 920.ccode != 0 predicts failure perfectly
      920.ccode dropped and 11 obs not used

note: 850.ccode omitted because of collinearity
{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-232.48455}  
Iteration 1:{space 3}log pseudolikelihood = {res: -163.2153}  
Iteration 2:{space 3}log pseudolikelihood = {res:-157.81136}  
Iteration 3:{space 3}log pseudolikelihood = {res:-157.69295}  
Iteration 4:{space 3}log pseudolikelihood = {res:-157.69291}  
Iteration 5:{space 3}log pseudolikelihood = {res:-157.69291}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       381
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(6)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-157.69291{txt}{col 49}Pseudo R2{col 67}= {res}    0.3217

{txt}{ralign 85:(Std. Err. adjusted for {res:51} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}             punish{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
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{txt}{space 19} {c |}
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{space 19} {c |}
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{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a41
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}   -232.485{col 42}{txt}Log-Lik Full Model:{col 69}{res}   -157.693
{txt}D(323):{col 28}{res}    315.386{col 42}{txt}LR(6):{col 69}{res}    149.583
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.000
{txt}McFadden's R2:{col 28}{res}      0.322{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.072
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.325{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.461
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.524{col 42}{txt}Efron's R2:{col 69}{res}      0.364
{txt}Variance of y*:{col 28}{res}      6.912{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.811{col 42}{txt}Adj Count R2:{col 69}{res}      0.368
{txt}AIC:{col 28}{res}      1.132{col 42}{txt}AIC*n:{col 69}{res}    431.386
{txt}BIC:{col 28}{res}  -1604.138{col 42}{txt}BIC':{col 69}{res}   -113.926
{txt}BIC used by Stata:{col 28}{res}    356.985{col 42}{txt}AIC used by Stata:{col 69}{res}    329.386
{txt}
{com}. 
. logit punish max_pers_magaloni max_military_scale gwf_democracy irr_entry max_purges  previous_sum_punish instit_control i.ccode, cluster(ccode)

{txt}note: 2.ccode != 0 predicts failure perfectly
      2.ccode dropped and 9 obs not used

note: 20.ccode != 0 predicts failure perfectly
      20.ccode dropped and 10 obs not used

note: 91.ccode != 0 predicts failure perfectly
      91.ccode dropped and 7 obs not used

note: 94.ccode != 0 predicts failure perfectly
      94.ccode dropped and 13 obs not used

note: 100.ccode != 0 predicts failure perfectly
      100.ccode dropped and 11 obs not used

note: 165.ccode != 0 predicts failure perfectly
      165.ccode dropped and 18 obs not used

note: 200.ccode != 0 predicts failure perfectly
      200.ccode dropped and 10 obs not used

note: 205.ccode != 0 predicts failure perfectly
      205.ccode dropped and 15 obs not used

note: 210.ccode != 0 predicts failure perfectly
      210.ccode dropped and 12 obs not used

note: 211.ccode != 0 predicts failure perfectly
      211.ccode dropped and 17 obs not used

note: 220.ccode != 0 predicts failure perfectly
      220.ccode dropped and 19 obs not used

note: 225.ccode != 0 predicts failure perfectly
      225.ccode dropped and 13 obs not used

note: 230.ccode != 0 predicts failure perfectly
      230.ccode dropped and 4 obs not used

note: 260.ccode != 0 predicts failure perfectly
      260.ccode dropped and 6 obs not used

note: 290.ccode != 0 predicts failure perfectly
      290.ccode dropped and 3 obs not used

note: 305.ccode != 0 predicts failure perfectly
      305.ccode dropped and 7 obs not used

note: 310.ccode != 0 predicts failure perfectly
      310.ccode dropped and 5 obs not used

note: 315.ccode != 0 predicts failure perfectly
      315.ccode dropped and 4 obs not used

note: 317.ccode != 0 predicts failure perfectly
      317.ccode dropped and 3 obs not used

note: 325.ccode != 0 predicts failure perfectly
      325.ccode dropped and 34 obs not used

note: 343.ccode != 0 predicts failure perfectly
      343.ccode dropped and 2 obs not used

note: 349.ccode != 0 predicts failure perfectly
      349.ccode dropped and 2 obs not used

note: 359.ccode != 0 predicts failure perfectly
      359.ccode dropped and 1 obs not used

note: 366.ccode != 0 predicts failure perfectly
      366.ccode dropped and 7 obs not used

note: 367.ccode != 0 predicts failure perfectly
      367.ccode dropped and 7 obs not used

note: 368.ccode != 0 predicts failure perfectly
      368.ccode dropped and 3 obs not used

note: 369.ccode != 0 predicts failure perfectly
      369.ccode dropped and 1 obs not used

note: 375.ccode != 0 predicts failure perfectly
      375.ccode dropped and 3 obs not used

note: 385.ccode != 0 predicts failure perfectly
      385.ccode dropped and 18 obs not used

note: 404.ccode != 0 predicts success perfectly
      404.ccode dropped and 2 obs not used

note: 420.ccode != 0 predicts success perfectly
      420.ccode dropped and 1 obs not used

note: 433.ccode != 0 predicts failure perfectly
      433.ccode dropped and 2 obs not used

note: 434.ccode != 0 predicts failure perfectly
      434.ccode dropped and 2 obs not used

note: 437.ccode != 0 predicts success perfectly
      437.ccode dropped and 2 obs not used

note: 450.ccode != 0 predicts success perfectly
      450.ccode dropped and 2 obs not used

note: 461.ccode != 0 predicts success perfectly
      461.ccode dropped and 1 obs not used

note: 481.ccode != 0 predicts success perfectly
      481.ccode dropped and 1 obs not used

note: 483.ccode != 0 predicts success perfectly
      483.ccode dropped and 3 obs not used

note: 500.ccode != 0 predicts success perfectly
      500.ccode dropped and 2 obs not used

note: 501.ccode != 0 predicts failure perfectly
      501.ccode dropped and 1 obs not used

note: 510.ccode != 0 predicts failure perfectly
      510.ccode dropped and 2 obs not used

note: 517.ccode != 0 predicts success perfectly
      517.ccode dropped and 2 obs not used

note: 530.ccode != 0 predicts success perfectly
      530.ccode dropped and 1 obs not used

note: 551.ccode != 0 predicts failure perfectly
      551.ccode dropped and 1 obs not used

note: 560.ccode != 0 predicts failure perfectly
      560.ccode dropped and 2 obs not used

note: 590.ccode != 0 predicts failure perfectly
      590.ccode dropped and 3 obs not used

note: 615.ccode != 0 predicts failure perfectly
      615.ccode dropped and 2 obs not used

note: 616.ccode != 0 predicts success perfectly
      616.ccode dropped and 1 obs not used

note: 620.ccode != 0 predicts success perfectly
      620.ccode dropped and 1 obs not used

note: 645.ccode != 0 predicts success perfectly
      645.ccode dropped and 2 obs not used

note: 651.ccode != 0 predicts success perfectly
      651.ccode dropped and 2 obs not used

note: 700.ccode != 0 predicts success perfectly
      700.ccode dropped and 1 obs not used

note: 712.ccode != 0 predicts failure perfectly
      712.ccode dropped and 1 obs not used

note: 790.ccode != 0 predicts failure perfectly
      790.ccode dropped and 2 obs not used

note: 811.ccode != 0 predicts failure perfectly
      811.ccode dropped and 1 obs not used

note: 816.ccode != 0 predicts failure perfectly
      816.ccode dropped and 3 obs not used

note: 817.ccode != 0 predicts success perfectly
      817.ccode dropped and 1 obs not used

note: 820.ccode != 0 predicts failure perfectly
      820.ccode dropped and 2 obs not used

note: 830.ccode != 0 predicts failure perfectly
      830.ccode dropped and 1 obs not used

note: 900.ccode != 0 predicts failure perfectly
      900.ccode dropped and 8 obs not used

note: 920.ccode != 0 predicts failure perfectly
      920.ccode dropped and 11 obs not used

note: 850.ccode omitted because of collinearity
{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-241.56949}  
Iteration 1:{space 3}log pseudolikelihood = {res:-153.18555}  
Iteration 2:{space 3}log pseudolikelihood = {res:-144.95792}  
Iteration 3:{space 3}log pseudolikelihood = {res: -144.4746}  
Iteration 4:{space 3}log pseudolikelihood = {res:-144.47406}  
Iteration 5:{space 3}log pseudolikelihood = {res:-144.47406}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       388
{txt}{col 49}{help j_robustsingular##|_new:Wald chi2(6)}{col 67}=          {res}.
{txt}{col 49}Prob > chi2{col 67}=          {res}.
{txt}Log pseudolikelihood = {res}-144.47406{txt}{col 49}Pseudo R2{col 67}= {res}    0.4019

{txt}{ralign 85:(Std. Err. adjusted for {res:52} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}             punish{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}max_pers_magaloni {c |}{col 21}{res}{space 2} 2.201533{col 33}{space 2} .4854572{col 44}{space 1}    4.53{col 53}{space 3}0.000{col 61}{space 4} 1.250054{col 74}{space 3} 3.153012
{txt}{space 1}max_military_scale {c |}{col 21}{res}{space 2} .9524251{col 33}{space 2} .9460172{col 44}{space 1}    1.01{col 53}{space 3}0.314{col 61}{space 4}-.9017344{col 74}{space 3} 2.806585
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2}   .83018{col 33}{space 2} .6760667{col 44}{space 1}    1.23{col 53}{space 3}0.219{col 61}{space 4}-.4948864{col 74}{space 3} 2.155246
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2}-1.625531{col 33}{space 2} .6357694{col 44}{space 1}   -2.56{col 53}{space 3}0.011{col 61}{space 4}-2.871616{col 74}{space 3}-.3794454
{txt}{space 9}max_purges {c |}{col 21}{res}{space 2} .0911365{col 33}{space 2} .0763605{col 44}{space 1}    1.19{col 53}{space 3}0.233{col 61}{space 4}-.0585273{col 74}{space 3} .2408004
{txt}previous_sum_punish {c |}{col 21}{res}{space 2}-.4716358{col 33}{space 2}  .107543{col 44}{space 1}   -4.39{col 53}{space 3}0.000{col 61}{space 4}-.6824163{col 74}{space 3}-.2608554
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2} .8304563{col 33}{space 2} .5394663{col 44}{space 1}    1.54{col 53}{space 3}0.124{col 61}{space 4}-.2268783{col 74}{space 3} 1.887791
{txt}{space 19} {c |}
{space 14}ccode {c |}
{space 17}2  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 16}20  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 16}41  {c |}{col 21}{res}{space 2} 9.534723{col 33}{space 2} 2.689444{col 44}{space 1}    3.55{col 53}{space 3}0.000{col 61}{space 4}  4.26351{col 74}{space 3} 14.80594
{txt}{space 16}42  {c |}{col 21}{res}{space 2} 5.571253{col 33}{space 2} 1.580113{col 44}{space 1}    3.53{col 53}{space 3}0.000{col 61}{space 4} 2.474288{col 74}{space 3} 8.668219
{txt}{space 16}70  {c |}{col 21}{res}{space 2}-.0967553{col 33}{space 2} .7534951{col 44}{space 1}   -0.13{col 53}{space 3}0.898{col 61}{space 4}-1.573579{col 74}{space 3} 1.380068
{txt}{space 16}90  {c |}{col 21}{res}{space 2}-.5973308{col 33}{space 2} .9397946{col 44}{space 1}   -0.64{col 53}{space 3}0.525{col 61}{space 4}-2.439294{col 74}{space 3} 1.244633
{txt}{space 16}91  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 16}92  {c |}{col 21}{res}{space 2} 2.856447{col 33}{space 2}  1.48538{col 44}{space 1}    1.92{col 53}{space 3}0.054{col 61}{space 4}-.0548437{col 74}{space 3} 5.767738
{txt}{space 16}93  {c |}{col 21}{res}{space 2} -.114066{col 33}{space 2} .8595755{col 44}{space 1}   -0.13{col 53}{space 3}0.894{col 61}{space 4}-1.798803{col 74}{space 3} 1.570671
{txt}{space 16}94  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 16}95  {c |}{col 21}{res}{space 2} 1.293873{col 33}{space 2} .6009309{col 44}{space 1}    2.15{col 53}{space 3}0.031{col 61}{space 4} .1160704{col 74}{space 3} 2.471676
{txt}{space 15}100  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}101  {c |}{col 21}{res}{space 2} 2.538817{col 33}{space 2} .8915305{col 44}{space 1}    2.85{col 53}{space 3}0.004{col 61}{space 4} .7914491{col 74}{space 3} 4.286184
{txt}{space 15}130  {c |}{col 21}{res}{space 2} 9.164149{col 33}{space 2} 2.483888{col 44}{space 1}    3.69{col 53}{space 3}0.000{col 61}{space 4} 4.295819{col 74}{space 3} 14.03248
{txt}{space 15}135  {c |}{col 21}{res}{space 2} 2.871795{col 33}{space 2} 1.211594{col 44}{space 1}    2.37{col 53}{space 3}0.018{col 61}{space 4} .4971134{col 74}{space 3} 5.246476
{txt}{space 15}140  {c |}{col 21}{res}{space 2}-5.217785{col 33}{space 2} 1.349628{col 44}{space 1}   -3.87{col 53}{space 3}0.000{col 61}{space 4}-7.863008{col 74}{space 3}-2.572562
{txt}{space 15}145  {c |}{col 21}{res}{space 2}  2.41898{col 33}{space 2} 1.194712{col 44}{space 1}    2.02{col 53}{space 3}0.043{col 61}{space 4} .0773869{col 74}{space 3} 4.760573
{txt}{space 15}150  {c |}{col 21}{res}{space 2} 3.397271{col 33}{space 2} .9122609{col 44}{space 1}    3.72{col 53}{space 3}0.000{col 61}{space 4} 1.609272{col 74}{space 3} 5.185269
{txt}{space 15}155  {c |}{col 21}{res}{space 2}-1.277866{col 33}{space 2} 1.118957{col 44}{space 1}   -1.14{col 53}{space 3}0.253{col 61}{space 4}-3.470982{col 74}{space 3} .9152489
{txt}{space 15}160  {c |}{col 21}{res}{space 2}-3.510818{col 33}{space 2}  .839618{col 44}{space 1}   -4.18{col 53}{space 3}0.000{col 61}{space 4}-5.156439{col 74}{space 3}-1.865197
{txt}{space 15}165  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}200  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}205  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}210  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}211  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}220  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}225  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}230  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}235  {c |}{col 21}{res}{space 2}-1.185529{col 33}{space 2}  .488715{col 44}{space 1}   -2.43{col 53}{space 3}0.015{col 61}{space 4}-2.143393{col 74}{space 3}-.2276655
{txt}{space 15}260  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}265  {c |}{col 21}{res}{space 2}-5.864919{col 33}{space 2} .9876187{col 44}{space 1}   -5.94{col 53}{space 3}0.000{col 61}{space 4}-7.800616{col 74}{space 3}-3.929222
{txt}{space 15}290  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}305  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}310  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}315  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}317  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}325  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}339  {c |}{col 21}{res}{space 2}-.1686845{col 33}{space 2} .6113231{col 44}{space 1}   -0.28{col 53}{space 3}0.783{col 61}{space 4}-1.366856{col 74}{space 3} 1.029487
{txt}{space 15}343  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}345  {c |}{col 21}{res}{space 2}-2.628978{col 33}{space 2} .6449515{col 44}{space 1}   -4.08{col 53}{space 3}0.000{col 61}{space 4}-3.893059{col 74}{space 3}-1.364896
{txt}{space 15}349  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}350  {c |}{col 21}{res}{space 2}-2.087962{col 33}{space 2} 1.191181{col 44}{space 1}   -1.75{col 53}{space 3}0.080{col 61}{space 4}-4.422634{col 74}{space 3} .2467095
{txt}{space 15}355  {c |}{col 21}{res}{space 2}-3.868747{col 33}{space 2} .5026417{col 44}{space 1}   -7.70{col 53}{space 3}0.000{col 61}{space 4}-4.853907{col 74}{space 3}-2.883588
{txt}{space 15}359  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}360  {c |}{col 21}{res}{space 2}-2.624579{col 33}{space 2} .3606609{col 44}{space 1}   -7.28{col 53}{space 3}0.000{col 61}{space 4}-3.331461{col 74}{space 3}-1.917697
{txt}{space 15}366  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}367  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}368  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}369  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}375  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}380  {c |}{col 21}{res}{space 2}-4.245219{col 33}{space 2}  .612249{col 44}{space 1}   -6.93{col 53}{space 3}0.000{col 61}{space 4}-5.445205{col 74}{space 3}-3.045233
{txt}{space 15}385  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}404  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}420  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}432  {c |}{col 21}{res}{space 2}-3.055278{col 33}{space 2} .5150359{col 44}{space 1}   -5.93{col 53}{space 3}0.000{col 61}{space 4} -4.06473{col 74}{space 3}-2.045826
{txt}{space 15}433  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}434  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}435  {c |}{col 21}{res}{space 2}-1.885718{col 33}{space 2}  .501289{col 44}{space 1}   -3.76{col 53}{space 3}0.000{col 61}{space 4}-2.868227{col 74}{space 3}-.9032099
{txt}{space 15}436  {c |}{col 21}{res}{space 2}-3.870701{col 33}{space 2} .7063825{col 44}{space 1}   -5.48{col 53}{space 3}0.000{col 61}{space 4}-5.255185{col 74}{space 3}-2.486217
{txt}{space 15}437  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}439  {c |}{col 21}{res}{space 2}-1.979039{col 33}{space 2} .6027845{col 44}{space 1}   -3.28{col 53}{space 3}0.001{col 61}{space 4}-3.160475{col 74}{space 3}-.7976027
{txt}{space 15}450  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}451  {c |}{col 21}{res}{space 2}-.5283785{col 33}{space 2}  .300498{col 44}{space 1}   -1.76{col 53}{space 3}0.079{col 61}{space 4}-1.117344{col 74}{space 3} .0605868
{txt}{space 15}452  {c |}{col 21}{res}{space 2}-3.689668{col 33}{space 2} .6212122{col 44}{space 1}   -5.94{col 53}{space 3}0.000{col 61}{space 4}-4.907222{col 74}{space 3}-2.472115
{txt}{space 15}461  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}475  {c |}{col 21}{res}{space 2}-1.626603{col 33}{space 2} .5207676{col 44}{space 1}   -3.12{col 53}{space 3}0.002{col 61}{space 4}-2.647289{col 74}{space 3} -.605917
{txt}{space 15}481  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}482  {c |}{col 21}{res}{space 2}  -3.8755{col 33}{space 2} .5427221{col 44}{space 1}   -7.14{col 53}{space 3}0.000{col 61}{space 4}-4.939215{col 74}{space 3}-2.811784
{txt}{space 15}483  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}484  {c |}{col 21}{res}{space 2}-2.430269{col 33}{space 2} .4689316{col 44}{space 1}   -5.18{col 53}{space 3}0.000{col 61}{space 4}-3.349358{col 74}{space 3} -1.51118
{txt}{space 15}490  {c |}{col 21}{res}{space 2}-5.578511{col 33}{space 2} .8530097{col 44}{space 1}   -6.54{col 53}{space 3}0.000{col 61}{space 4}-7.250379{col 74}{space 3}-3.906643
{txt}{space 15}500  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}501  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}510  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}516  {c |}{col 21}{res}{space 2}-2.973416{col 33}{space 2} .5657279{col 44}{space 1}   -5.26{col 53}{space 3}0.000{col 61}{space 4}-4.082223{col 74}{space 3} -1.86461
{txt}{space 15}517  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}520  {c |}{col 21}{res}{space 2}-3.626986{col 33}{space 2} .6059884{col 44}{space 1}   -5.99{col 53}{space 3}0.000{col 61}{space 4}-4.814701{col 74}{space 3} -2.43927
{txt}{space 15}530  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}551  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}553  {c |}{col 21}{res}{space 2}-3.786407{col 33}{space 2} .5710799{col 44}{space 1}   -6.63{col 53}{space 3}0.000{col 61}{space 4}-4.905703{col 74}{space 3}-2.667111
{txt}{space 15}560  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}570  {c |}{col 21}{res}{space 2} -4.05216{col 33}{space 2} .8093486{col 44}{space 1}   -5.01{col 53}{space 3}0.000{col 61}{space 4}-5.638454{col 74}{space 3}-2.465866
{txt}{space 15}580  {c |}{col 21}{res}{space 2}-3.546655{col 33}{space 2} .4972338{col 44}{space 1}   -7.13{col 53}{space 3}0.000{col 61}{space 4}-4.521215{col 74}{space 3}-2.572094
{txt}{space 15}590  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}615  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}616  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}620  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}625  {c |}{col 21}{res}{space 2}-2.940328{col 33}{space 2} .4847618{col 44}{space 1}   -6.07{col 53}{space 3}0.000{col 61}{space 4}-3.890444{col 74}{space 3}-1.990213
{txt}{space 15}640  {c |}{col 21}{res}{space 2} .0474846{col 33}{space 2} .4808146{col 44}{space 1}    0.10{col 53}{space 3}0.921{col 61}{space 4}-.8948947{col 74}{space 3} .9898639
{txt}{space 15}645  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}651  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}652  {c |}{col 21}{res}{space 2}-2.236079{col 33}{space 2} .4679392{col 44}{space 1}   -4.78{col 53}{space 3}0.000{col 61}{space 4}-3.153223{col 74}{space 3}-1.318935
{txt}{space 15}666  {c |}{col 21}{res}{space 2}-4.985814{col 33}{space 2} .5743742{col 44}{space 1}   -8.68{col 53}{space 3}0.000{col 61}{space 4}-6.111567{col 74}{space 3}-3.860061
{txt}{space 15}700  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}712  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}732  {c |}{col 21}{res}{space 2}-3.209662{col 33}{space 2} .4907964{col 44}{space 1}   -6.54{col 53}{space 3}0.000{col 61}{space 4}-4.171606{col 74}{space 3}-2.247719
{txt}{space 15}740  {c |}{col 21}{res}{space 2}-3.302211{col 33}{space 2} .3278816{col 44}{space 1}  -10.07{col 53}{space 3}0.000{col 61}{space 4}-3.944847{col 74}{space 3}-2.659575
{txt}{space 15}750  {c |}{col 21}{res}{space 2}-3.463719{col 33}{space 2} .5116248{col 44}{space 1}   -6.77{col 53}{space 3}0.000{col 61}{space 4}-4.466485{col 74}{space 3}-2.460953
{txt}{space 15}770  {c |}{col 21}{res}{space 2}-3.523187{col 33}{space 2} .4111648{col 44}{space 1}   -8.57{col 53}{space 3}0.000{col 61}{space 4}-4.329056{col 74}{space 3}-2.717319
{txt}{space 15}771  {c |}{col 21}{res}{space 2}-3.978483{col 33}{space 2} .6453223{col 44}{space 1}   -6.17{col 53}{space 3}0.000{col 61}{space 4}-5.243292{col 74}{space 3}-2.713675
{txt}{space 15}775  {c |}{col 21}{res}{space 2}-5.502053{col 33}{space 2} .7254839{col 44}{space 1}   -7.58{col 53}{space 3}0.000{col 61}{space 4}-6.923976{col 74}{space 3}-4.080131
{txt}{space 15}780  {c |}{col 21}{res}{space 2}-2.783932{col 33}{space 2} .5432921{col 44}{space 1}   -5.12{col 53}{space 3}0.000{col 61}{space 4}-3.848765{col 74}{space 3}-1.719099
{txt}{space 15}790  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}800  {c |}{col 21}{res}{space 2}-4.951912{col 33}{space 2} .7630879{col 44}{space 1}   -6.49{col 53}{space 3}0.000{col 61}{space 4}-6.447536{col 74}{space 3}-3.456287
{txt}{space 15}811  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}816  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}817  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}820  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}830  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}840  {c |}{col 21}{res}{space 2}-2.870112{col 33}{space 2} .5742888{col 44}{space 1}   -5.00{col 53}{space 3}0.000{col 61}{space 4}-3.995697{col 74}{space 3}-1.744526
{txt}{space 15}850  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (omitted)
{space 15}900  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 15}920  {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (empty)
{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2} .4946917{col 33}{space 2} .7110693{col 44}{space 1}    0.70{col 53}{space 3}0.487{col 61}{space 4}-.8989785{col 74}{space 3} 1.888362
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a42
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}   -241.569{col 42}{txt}Log-Lik Full Model:{col 69}{res}   -144.474
{txt}D(329):{col 28}{res}    288.948{col 42}{txt}LR(6):{col 69}{res}    194.191
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.000
{txt}McFadden's R2:{col 28}{res}      0.402{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.158
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.394{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.553
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.654{col 42}{txt}Efron's R2:{col 69}{res}      0.443
{txt}Variance of y*:{col 28}{res}      9.498{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.830{col 42}{txt}Adj Count R2:{col 69}{res}      0.459
{txt}AIC:{col 28}{res}      1.049{col 42}{txt}AIC*n:{col 69}{res}    406.948
{txt}BIC:{col 28}{res}  -1672.223{col 42}{txt}BIC':{col 69}{res}   -158.425
{txt}BIC used by Stata:{col 28}{res}    330.675{col 42}{txt}AIC used by Stata:{col 69}{res}    302.948
{txt}
{com}. 
. logit punish max_person_scale max_military_scale gwf_democracy irr_entry max_purges previous_sum_punish instit_control eap eca lac mena sa ssa we_na, cluster(ccode)

{txt}note: we_na omitted because of collinearity
{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-353.29552}  
Iteration 1:{space 3}log pseudolikelihood = {res: -241.9876}  
Iteration 2:{space 3}log pseudolikelihood = {res: -226.9534}  
Iteration 3:{space 3}log pseudolikelihood = {res:-225.22424}  
Iteration 4:{space 3}log pseudolikelihood = {res: -225.1925}  
Iteration 5:{space 3}log pseudolikelihood = {res:-225.19242}  
Iteration 6:{space 3}log pseudolikelihood = {res:-225.19242}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       727
{txt}{col 49}Wald chi2({res}13{txt}){col 67}= {res}    169.75
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-225.19242{txt}{col 49}Pseudo R2{col 67}= {res}    0.3626

{txt}{ralign 85:(Std. Err. adjusted for {res:112} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}             punish{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}max_person_scale {c |}{col 21}{res}{space 2} 2.147246{col 33}{space 2} .3932376{col 44}{space 1}    5.46{col 53}{space 3}0.000{col 61}{space 4} 1.376515{col 74}{space 3} 2.917978
{txt}{space 1}max_military_scale {c |}{col 21}{res}{space 2} 1.989805{col 33}{space 2} .3845541{col 44}{space 1}    5.17{col 53}{space 3}0.000{col 61}{space 4} 1.236093{col 74}{space 3} 2.743517
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2}-.3420253{col 33}{space 2} .3474651{col 44}{space 1}   -0.98{col 53}{space 3}0.325{col 61}{space 4}-1.023044{col 74}{space 3} .3389938
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2}-1.185961{col 33}{space 2} .4163249{col 44}{space 1}   -2.85{col 53}{space 3}0.004{col 61}{space 4}-2.001943{col 74}{space 3}-.3699796
{txt}{space 9}max_purges {c |}{col 21}{res}{space 2} .0712337{col 33}{space 2}  .074591{col 44}{space 1}    0.95{col 53}{space 3}0.340{col 61}{space 4}-.0749619{col 74}{space 3} .2174294
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0760416{col 33}{space 2} .0178764{col 44}{space 1}    4.25{col 53}{space 3}0.000{col 61}{space 4} .0410045{col 74}{space 3} .1110787
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.2793404{col 33}{space 2}  .350312{col 44}{space 1}   -0.80{col 53}{space 3}0.425{col 61}{space 4}-.9659394{col 74}{space 3} .4072585
{txt}{space 16}eap {c |}{col 21}{res}{space 2}  1.29422{col 33}{space 2} .6520177{col 44}{space 1}    1.98{col 53}{space 3}0.047{col 61}{space 4} .0162891{col 74}{space 3} 2.572151
{txt}{space 16}eca {c |}{col 21}{res}{space 2}  1.72354{col 33}{space 2} .6018403{col 44}{space 1}    2.86{col 53}{space 3}0.004{col 61}{space 4}  .543955{col 74}{space 3} 2.903126
{txt}{space 16}lac {c |}{col 21}{res}{space 2} 1.052986{col 33}{space 2} .6378191{col 44}{space 1}    1.65{col 53}{space 3}0.099{col 61}{space 4}-.1971165{col 74}{space 3} 2.303089
{txt}{space 15}mena {c |}{col 21}{res}{space 2} 2.230664{col 33}{space 2} .7239082{col 44}{space 1}    3.08{col 53}{space 3}0.002{col 61}{space 4} .8118302{col 74}{space 3} 3.649498
{txt}{space 17}sa {c |}{col 21}{res}{space 2}  2.42736{col 33}{space 2} .5731512{col 44}{space 1}    4.24{col 53}{space 3}0.000{col 61}{space 4} 1.304004{col 74}{space 3} 3.550716
{txt}{space 16}ssa {c |}{col 21}{res}{space 2} 2.437297{col 33}{space 2} .6139706{col 44}{space 1}    3.97{col 53}{space 3}0.000{col 61}{space 4} 1.233937{col 74}{space 3} 3.640657
{txt}{space 14}we_na {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (omitted)
{space 14}_cons {c |}{col 21}{res}{space 2}-3.812229{col 33}{space 2} .6629737{col 44}{space 1}   -5.75{col 53}{space 3}0.000{col 61}{space 4}-5.111634{col 74}{space 3}-2.512825
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a43
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}   -353.296{col 42}{txt}Log-Lik Full Model:{col 69}{res}   -225.192
{txt}D(713):{col 28}{res}    450.385{col 42}{txt}LR(13):{col 69}{res}    256.206
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.000
{txt}McFadden's R2:{col 28}{res}      0.363{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.323
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.297{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.478
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.499{col 42}{txt}Efron's R2:{col 69}{res}      0.376
{txt}Variance of y*:{col 28}{res}      6.561{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.872{col 42}{txt}Adj Count R2:{col 69}{res}      0.326
{txt}AIC:{col 28}{res}      0.658{col 42}{txt}AIC*n:{col 69}{res}    478.385
{txt}BIC:{col 28}{res}  -4247.520{col 42}{txt}BIC':{col 69}{res}   -170.550
{txt}BIC used by Stata:{col 28}{res}    542.630{col 42}{txt}AIC used by Stata:{col 69}{res}    478.385
{txt}
{com}. 
. logit punish max_pers_magaloni max_military_scale gwf_democracy irr_entry max_purges  previous_sum_punish instit_control eap eca lac mena sa ssa we_na, cluster(ccode)

{txt}note: we_na omitted because of collinearity
{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-363.95232}  
Iteration 1:{space 3}log pseudolikelihood = {res:-237.75419}  
Iteration 2:{space 3}log pseudolikelihood = {res:-221.03536}  
Iteration 3:{space 3}log pseudolikelihood = {res:-218.67855}  
Iteration 4:{space 3}log pseudolikelihood = {res:-218.64087}  
Iteration 5:{space 3}log pseudolikelihood = {res:-218.64087}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       718
{txt}{col 49}Wald chi2({res}13{txt}){col 67}= {res}    180.01
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-218.64087{txt}{col 49}Pseudo R2{col 67}= {res}    0.3993

{txt}{ralign 85:(Std. Err. adjusted for {res:112} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}             punish{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}max_pers_magaloni {c |}{col 21}{res}{space 2} 1.705989{col 33}{space 2} .2489162{col 44}{space 1}    6.85{col 53}{space 3}0.000{col 61}{space 4} 1.218122{col 74}{space 3} 2.193856
{txt}{space 1}max_military_scale {c |}{col 21}{res}{space 2} .2988741{col 33}{space 2} .4502284{col 44}{space 1}    0.66{col 53}{space 3}0.507{col 61}{space 4}-.5835574{col 74}{space 3} 1.181306
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2} .2864132{col 33}{space 2} .3187202{col 44}{space 1}    0.90{col 53}{space 3}0.369{col 61}{space 4}-.3382669{col 74}{space 3} .9110933
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2}-.8024863{col 33}{space 2}  .352474{col 44}{space 1}   -2.28{col 53}{space 3}0.023{col 61}{space 4}-1.493323{col 74}{space 3}  -.11165
{txt}{space 9}max_purges {c |}{col 21}{res}{space 2} .0637874{col 33}{space 2} .1313744{col 44}{space 1}    0.49{col 53}{space 3}0.627{col 61}{space 4}-.1937017{col 74}{space 3} .3212766
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0639251{col 33}{space 2} .0187827{col 44}{space 1}    3.40{col 53}{space 3}0.001{col 61}{space 4} .0271117{col 74}{space 3} .1007386
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2} -.262676{col 33}{space 2} .3796363{col 44}{space 1}   -0.69{col 53}{space 3}0.489{col 61}{space 4} -1.00675{col 74}{space 3} .4813976
{txt}{space 16}eap {c |}{col 21}{res}{space 2} 1.502586{col 33}{space 2} .6762914{col 44}{space 1}    2.22{col 53}{space 3}0.026{col 61}{space 4} .1770798{col 74}{space 3} 2.828093
{txt}{space 16}eca {c |}{col 21}{res}{space 2} 1.667543{col 33}{space 2} .5480719{col 44}{space 1}    3.04{col 53}{space 3}0.002{col 61}{space 4} .5933415{col 74}{space 3} 2.741744
{txt}{space 16}lac {c |}{col 21}{res}{space 2}  1.41514{col 33}{space 2} .5387345{col 44}{space 1}    2.63{col 53}{space 3}0.009{col 61}{space 4} .3592397{col 74}{space 3}  2.47104
{txt}{space 15}mena {c |}{col 21}{res}{space 2}  2.70799{col 33}{space 2}  .779905{col 44}{space 1}    3.47{col 53}{space 3}0.001{col 61}{space 4} 1.179405{col 74}{space 3} 4.236576
{txt}{space 17}sa {c |}{col 21}{res}{space 2} 2.869582{col 33}{space 2}  .556403{col 44}{space 1}    5.16{col 53}{space 3}0.000{col 61}{space 4} 1.779052{col 74}{space 3} 3.960112
{txt}{space 16}ssa {c |}{col 21}{res}{space 2}  2.98112{col 33}{space 2} .5495263{col 44}{space 1}    5.42{col 53}{space 3}0.000{col 61}{space 4} 1.904068{col 74}{space 3} 4.058171
{txt}{space 14}we_na {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (omitted)
{space 14}_cons {c |}{col 21}{res}{space 2}-4.811545{col 33}{space 2} .6633029{col 44}{space 1}   -7.25{col 53}{space 3}0.000{col 61}{space 4}-6.111594{col 74}{space 3}-3.511495
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a44
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}   -363.952{col 42}{txt}Log-Lik Full Model:{col 69}{res}   -218.641
{txt}D(704):{col 28}{res}    437.282{col 42}{txt}LR(13):{col 69}{res}    290.623
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.000
{txt}McFadden's R2:{col 28}{res}      0.399{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.361
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.333{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.522
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.550{col 42}{txt}Efron's R2:{col 69}{res}      0.413
{txt}Variance of y*:{col 28}{res}      7.312{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.873{col 42}{txt}Adj Count R2:{col 69}{res}      0.381
{txt}AIC:{col 28}{res}      0.648{col 42}{txt}AIC*n:{col 69}{res}    465.282
{txt}BIC:{col 28}{res}  -4192.553{col 42}{txt}BIC':{col 69}{res}   -205.129
{txt}BIC used by Stata:{col 28}{res}    529.352{col 42}{txt}AIC used by Stata:{col 69}{res}    465.282
{txt}
{com}. 
{txt}end of do-file

{com}. do "C:\Users\mtr012\AppData\Local\Temp\STD00000000.tmp"
{txt}
{com}. *Table A18
. logit punish gwf_personalist irr_entry max_purges  previous_sum_punish instit_control i.successor, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-464.59264}  
Iteration 1:{space 3}log pseudolikelihood = {res:-337.65019}  
Iteration 2:{space 3}log pseudolikelihood = {res:-327.13792}  
Iteration 3:{space 3}log pseudolikelihood = {res:-326.79427}  
Iteration 4:{space 3}log pseudolikelihood = {res:-326.79376}  
Iteration 5:{space 3}log pseudolikelihood = {res:-326.79376}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       903
{txt}{col 49}Wald chi2({res}9{txt}){col 67}= {res}    132.16
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-326.79376{txt}{col 49}Pseudo R2{col 67}= {res}    0.2966

{txt}{ralign 85:(Std. Err. adjusted for {res:124} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}             punish{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 4}gwf_personalist {c |}{col 21}{res}{space 2}  .895212{col 33}{space 2} .4934768{col 44}{space 1}    1.81{col 53}{space 3}0.070{col 61}{space 4}-.0719847{col 74}{space 3} 1.862409
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2} .0305178{col 33}{space 2} .3429316{col 44}{space 1}    0.09{col 53}{space 3}0.929{col 61}{space 4}-.6416158{col 74}{space 3} .7026514
{txt}{space 9}max_purges {c |}{col 21}{res}{space 2} .1318952{col 33}{space 2} .1891841{col 44}{space 1}    0.70{col 53}{space 3}0.486{col 61}{space 4}-.2388989{col 74}{space 3} .5026892
{txt}previous_sum_punish {c |}{col 21}{res}{space 2}  .041124{col 33}{space 2} .0178569{col 44}{space 1}    2.30{col 53}{space 3}0.021{col 61}{space 4}  .006125{col 74}{space 3} .0761229
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.5010426{col 33}{space 2} .3569555{col 44}{space 1}   -1.40{col 53}{space 3}0.160{col 61}{space 4}-1.200663{col 74}{space 3} .1985773
{txt}{space 19} {c |}
{space 10}successor {c |}
{space 17}2  {c |}{col 21}{res}{space 2}-1.224943{col 33}{space 2} .4201017{col 44}{space 1}   -2.92{col 53}{space 3}0.004{col 61}{space 4}-2.048327{col 74}{space 3}-.4015588
{txt}{space 17}3  {c |}{col 21}{res}{space 2}-.8558373{col 33}{space 2} .7081181{col 44}{space 1}   -1.21{col 53}{space 3}0.227{col 61}{space 4}-2.243723{col 74}{space 3} .5320487
{txt}{space 17}4  {c |}{col 21}{res}{space 2} -.276662{col 33}{space 2} .4843889{col 44}{space 1}   -0.57{col 53}{space 3}0.568{col 61}{space 4}-1.226047{col 74}{space 3} .6727228
{txt}{space 17}5  {c |}{col 21}{res}{space 2} -2.88119{col 33}{space 2} .4714002{col 44}{space 1}   -6.11{col 53}{space 3}0.000{col 61}{space 4}-3.805118{col 74}{space 3}-1.957263
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2} .6504198{col 33}{space 2} .4732632{col 44}{space 1}    1.37{col 53}{space 3}0.169{col 61}{space 4} -.277159{col 74}{space 3} 1.577999
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a45
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}   -464.593{col 42}{txt}Log-Lik Full Model:{col 69}{res}   -326.794
{txt}D(893):{col 28}{res}    653.588{col 42}{txt}LR(9):{col 69}{res}    275.598
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.000
{txt}McFadden's R2:{col 28}{res}      0.297{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.275
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.263{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.409
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.362{col 42}{txt}Efron's R2:{col 69}{res}      0.338
{txt}Variance of y*:{col 28}{res}      5.158{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.852{col 42}{txt}Adj Count R2:{col 69}{res}      0.295
{txt}AIC:{col 28}{res}      0.746{col 42}{txt}AIC*n:{col 69}{res}    673.588
{txt}BIC:{col 28}{res}  -5423.923{col 42}{txt}BIC':{col 69}{res}   -214.346
{txt}BIC used by Stata:{col 28}{res}    721.645{col 42}{txt}AIC used by Stata:{col 69}{res}    673.588
{txt}
{com}. 
. logit punish max_person_scale max_military_scale gwf_democracy irr_entry max_purges previous_sum_punish instit_control i.successor, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-342.40659}  
Iteration 1:{space 3}log pseudolikelihood = {res:-226.57564}  
Iteration 2:{space 3}log pseudolikelihood = {res:-213.69767}  
Iteration 3:{space 3}log pseudolikelihood = {res:-212.53758}  
Iteration 4:{space 3}log pseudolikelihood = {res:-212.53562}  
Iteration 5:{space 3}log pseudolikelihood = {res:-212.53562}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       717
{txt}{col 49}Wald chi2({res}10{txt}){col 67}= {res}    160.26
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-212.53562{txt}{col 49}Pseudo R2{col 67}= {res}    0.3793

{txt}{ralign 85:(Std. Err. adjusted for {res:113} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}             punish{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}max_person_scale {c |}{col 21}{res}{space 2} 1.935355{col 33}{space 2} .4739645{col 44}{space 1}    4.08{col 53}{space 3}0.000{col 61}{space 4} 1.006401{col 74}{space 3} 2.864308
{txt}{space 1}max_military_scale {c |}{col 21}{res}{space 2} 1.241117{col 33}{space 2} .4371432{col 44}{space 1}    2.84{col 53}{space 3}0.005{col 61}{space 4} .3843323{col 74}{space 3} 2.097902
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2} .3819903{col 33}{space 2} .5107255{col 44}{space 1}    0.75{col 53}{space 3}0.454{col 61}{space 4}-.6190132{col 74}{space 3} 1.382994
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2}-.4876652{col 33}{space 2} .5266167{col 44}{space 1}   -0.93{col 53}{space 3}0.354{col 61}{space 4}-1.519815{col 74}{space 3} .5444845
{txt}{space 9}max_purges {c |}{col 21}{res}{space 2} .0064739{col 33}{space 2} .0523146{col 44}{space 1}    0.12{col 53}{space 3}0.902{col 61}{space 4}-.0960608{col 74}{space 3} .1090085
{txt}previous_sum_punish {c |}{col 21}{res}{space 2}  .036962{col 33}{space 2} .0169038{col 44}{space 1}    2.19{col 53}{space 3}0.029{col 61}{space 4} .0038312{col 74}{space 3} .0700929
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.3407992{col 33}{space 2} .3940417{col 44}{space 1}   -0.86{col 53}{space 3}0.387{col 61}{space 4}-1.113107{col 74}{space 3} .4315084
{txt}{space 19} {c |}
{space 10}successor {c |}
{space 17}2  {c |}{col 21}{res}{space 2}-.6555912{col 33}{space 2} .6910323{col 44}{space 1}   -0.95{col 53}{space 3}0.343{col 61}{space 4} -2.00999{col 74}{space 3} .6988073
{txt}{space 17}4  {c |}{col 21}{res}{space 2}-.4879079{col 33}{space 2} .6540199{col 44}{space 1}   -0.75{col 53}{space 3}0.456{col 61}{space 4}-1.769763{col 74}{space 3} .7939475
{txt}{space 17}5  {c |}{col 21}{res}{space 2}-2.610813{col 33}{space 2} .7404326{col 44}{space 1}   -3.53{col 53}{space 3}0.000{col 61}{space 4}-4.062034{col 74}{space 3}-1.159592
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2}-.6003354{col 33}{space 2} .8464784{col 44}{space 1}   -0.71{col 53}{space 3}0.478{col 61}{space 4}-2.259403{col 74}{space 3} 1.058732
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a46
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}   -342.407{col 42}{txt}Log-Lik Full Model:{col 69}{res}   -212.536
{txt}D(706):{col 28}{res}    425.071{col 42}{txt}LR(10):{col 69}{res}    259.742
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.000
{txt}McFadden's R2:{col 28}{res}      0.379{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.347
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.304{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.494
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.430{col 42}{txt}Efron's R2:{col 69}{res}      0.419
{txt}Variance of y*:{col 28}{res}      5.772{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.888{col 42}{txt}Adj Count R2:{col 69}{res}      0.394
{txt}AIC:{col 28}{res}      0.624{col 42}{txt}AIC*n:{col 69}{res}    447.071
{txt}BIC:{col 28}{res}  -4216.932{col 42}{txt}BIC':{col 69}{res}   -193.991
{txt}BIC used by Stata:{col 28}{res}    497.397{col 42}{txt}AIC used by Stata:{col 69}{res}    447.071
{txt}
{com}. 
. logit punish max_pers_magaloni max_military_scale gwf_democracy irr_entry max_purges  previous_sum_punish instit_control i.successor, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-351.83502}  
Iteration 1:{space 3}log pseudolikelihood = {res:-224.98228}  
Iteration 2:{space 3}log pseudolikelihood = {res:-212.80233}  
Iteration 3:{space 3}log pseudolikelihood = {res:-212.09036}  
Iteration 4:{space 3}log pseudolikelihood = {res:-212.08847}  
Iteration 5:{space 3}log pseudolikelihood = {res:-212.08847}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       707
{txt}{col 49}Wald chi2({res}10{txt}){col 67}= {res}    231.97
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-212.08847{txt}{col 49}Pseudo R2{col 67}= {res}    0.3972

{txt}{ralign 85:(Std. Err. adjusted for {res:113} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}             punish{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}max_pers_magaloni {c |}{col 21}{res}{space 2} 1.469865{col 33}{space 2} .2713665{col 44}{space 1}    5.42{col 53}{space 3}0.000{col 61}{space 4} .9379969{col 74}{space 3} 2.001734
{txt}{space 1}max_military_scale {c |}{col 21}{res}{space 2} .1384161{col 33}{space 2} .4415482{col 44}{space 1}    0.31{col 53}{space 3}0.754{col 61}{space 4}-.7270025{col 74}{space 3} 1.003835
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2} .6201112{col 33}{space 2} .4381876{col 44}{space 1}    1.42{col 53}{space 3}0.157{col 61}{space 4}-.2387208{col 74}{space 3} 1.478943
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2}-.2700444{col 33}{space 2} .4535173{col 44}{space 1}   -0.60{col 53}{space 3}0.552{col 61}{space 4}-1.158922{col 74}{space 3} .6188331
{txt}{space 9}max_purges {c |}{col 21}{res}{space 2} .0100633{col 33}{space 2} .0818512{col 44}{space 1}    0.12{col 53}{space 3}0.902{col 61}{space 4}-.1503621{col 74}{space 3} .1704886
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0167851{col 33}{space 2} .0186678{col 44}{space 1}    0.90{col 53}{space 3}0.369{col 61}{space 4}-.0198031{col 74}{space 3} .0533732
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.1444137{col 33}{space 2} .4138686{col 44}{space 1}   -0.35{col 53}{space 3}0.727{col 61}{space 4}-.9555812{col 74}{space 3} .6667539
{txt}{space 19} {c |}
{space 10}successor {c |}
{space 17}2  {c |}{col 21}{res}{space 2}-1.430633{col 33}{space 2} .6307995{col 44}{space 1}   -2.27{col 53}{space 3}0.023{col 61}{space 4}-2.666977{col 74}{space 3}-.1942889
{txt}{space 17}4  {c |}{col 21}{res}{space 2}-1.345174{col 33}{space 2} .5532012{col 44}{space 1}   -2.43{col 53}{space 3}0.015{col 61}{space 4}-2.429429{col 74}{space 3}-.2609197
{txt}{space 17}5  {c |}{col 21}{res}{space 2}-2.807761{col 33}{space 2} .6132353{col 44}{space 1}   -4.58{col 53}{space 3}0.000{col 61}{space 4} -4.00968{col 74}{space 3}-1.605842
{txt}{space 19} {c |}
{space 14}_cons {c |}{col 21}{res}{space 2}-.8156064{col 33}{space 2} .8032234{col 44}{space 1}   -1.02{col 53}{space 3}0.310{col 61}{space 4}-2.389895{col 74}{space 3} .7586826
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a47
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}   -351.835{col 42}{txt}Log-Lik Full Model:{col 69}{res}   -212.088
{txt}D(696):{col 28}{res}    424.177{col 42}{txt}LR(10):{col 69}{res}    279.493
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.000
{txt}McFadden's R2:{col 28}{res}      0.397{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.366
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.327{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.518
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.459{col 42}{txt}Efron's R2:{col 69}{res}      0.434
{txt}Variance of y*:{col 28}{res}      6.077{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.876{col 42}{txt}Adj Count R2:{col 69}{res}      0.371
{txt}AIC:{col 28}{res}      0.631{col 42}{txt}AIC*n:{col 69}{res}    446.177
{txt}BIC:{col 28}{res}  -4142.300{col 42}{txt}BIC':{col 69}{res}   -213.883
{txt}BIC used by Stata:{col 28}{res}    496.348{col 42}{txt}AIC used by Stata:{col 69}{res}    446.177
{txt}
{com}. 
{txt}end of do-file

{com}. do "C:\Users\mtr012\AppData\Local\Temp\STD00000000.tmp"
{txt}
{com}. *Table A19
. 
. logit punish max_person_scale  if regime==2, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-84.696035}  
Iteration 1:{space 3}log pseudolikelihood = {res:-75.280095}  
Iteration 2:{space 3}log pseudolikelihood = {res:-75.219796}  
Iteration 3:{space 3}log pseudolikelihood = {res:-75.219783}  
Iteration 4:{space 3}log pseudolikelihood = {res:-75.219783}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       132
{txt}{col 49}Wald chi2({res}1{txt}){col 67}= {res}      7.72
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0055
{txt}Log pseudolikelihood = {res}-75.219783{txt}{col 49}Pseudo R2{col 67}= {res}    0.1119

{txt}{ralign 82:(Std. Err. adjusted for {res:45} clusters in ccode)}
{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}          punish{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
max_person_scale {c |}{col 18}{res}{space 2} 2.077927{col 30}{space 2} .7480452{col 41}{space 1}    2.78{col 50}{space 3}0.005{col 58}{space 4} .6117856{col 71}{space 3} 3.544069
{txt}{space 11}_cons {c |}{col 18}{res}{space 2}-1.309723{col 30}{space 2} .3174773{col 41}{space 1}   -4.13{col 50}{space 3}0.000{col 58}{space 4}-1.931967{col 71}{space 3}-.6874789
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a48
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}    -84.696{col 42}{txt}Log-Lik Full Model:{col 69}{res}    -75.220
{txt}D(130):{col 28}{res}    150.440{col 42}{txt}LR(1):{col 69}{res}     18.953
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.000
{txt}McFadden's R2:{col 28}{res}      0.112{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.088
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.134{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.185
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.165{col 42}{txt}Efron's R2:{col 69}{res}      0.147
{txt}Variance of y*:{col 28}{res}      3.941{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.720{col 42}{txt}Adj Count R2:{col 69}{res}      0.178
{txt}AIC:{col 28}{res}      1.170{col 42}{txt}AIC*n:{col 69}{res}    154.440
{txt}BIC:{col 28}{res}   -484.325{col 42}{txt}BIC':{col 69}{res}    -14.070
{txt}BIC used by Stata:{col 28}{res}    160.205{col 42}{txt}AIC used by Stata:{col 69}{res}    154.440
{txt}
{com}. 
. logit punish max_person_scale  if regime==4, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-90.523722}  
Iteration 1:{space 3}log pseudolikelihood = {res:-87.803057}  
Iteration 2:{space 3}log pseudolikelihood = {res:-87.802064}  
Iteration 3:{space 3}log pseudolikelihood = {res:-87.802064}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       133
{txt}{col 49}Wald chi2({res}1{txt}){col 67}= {res}      4.57
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0325
{txt}Log pseudolikelihood = {res}-87.802064{txt}{col 49}Pseudo R2{col 67}= {res}    0.0301

{txt}{ralign 82:(Std. Err. adjusted for {res:36} clusters in ccode)}
{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}          punish{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
max_person_scale {c |}{col 18}{res}{space 2} 1.182768{col 30}{space 2} .5532475{col 41}{space 1}    2.14{col 50}{space 3}0.033{col 58}{space 4} .0984228{col 71}{space 3} 2.267113
{txt}{space 11}_cons {c |}{col 18}{res}{space 2}-.6408171{col 30}{space 2}  .273108{col 41}{space 1}   -2.35{col 50}{space 3}0.019{col 58}{space 4}-1.176099{col 71}{space 3}-.1055352
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a49
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}    -90.524{col 42}{txt}Log-Lik Full Model:{col 69}{res}    -87.802
{txt}D(131):{col 28}{res}    175.604{col 42}{txt}LR(1):{col 69}{res}      5.443
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.020
{txt}McFadden's R2:{col 28}{res}      0.030{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.008
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.040{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.054
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.049{col 42}{txt}Efron's R2:{col 69}{res}      0.040
{txt}Variance of y*:{col 28}{res}      3.461{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.586{col 42}{txt}Adj Count R2:{col 69}{res}      0.018
{txt}AIC:{col 28}{res}      1.350{col 42}{txt}AIC*n:{col 69}{res}    179.604
{txt}BIC:{col 28}{res}   -465.032{col 42}{txt}BIC':{col 69}{res}     -0.553
{txt}BIC used by Stata:{col 28}{res}    185.385{col 42}{txt}AIC used by Stata:{col 69}{res}    179.604
{txt}
{com}. 
. logit punish max_person_scale if regime==5, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-181.72949}  
Iteration 1:{space 3}log pseudolikelihood = {res:-165.26788}  
Iteration 2:{space 3}log pseudolikelihood = {res:-152.26618}  
Iteration 3:{space 3}log pseudolikelihood = {res: -152.1136}  
Iteration 4:{space 3}log pseudolikelihood = {res:-152.11308}  
Iteration 5:{space 3}log pseudolikelihood = {res:-152.11308}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       557
{txt}{col 49}Wald chi2({res}1{txt}){col 67}= {res}     24.31
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-152.11308{txt}{col 49}Pseudo R2{col 67}= {res}    0.1630

{txt}{ralign 82:(Std. Err. adjusted for {res:84} clusters in ccode)}
{hline 17}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 18}{c |}{col 30}    Robust
{col 1}          punish{col 18}{c |}      Coef.{col 30}   Std. Err.{col 42}      z{col 50}   P>|z|{col 58}     [95% Con{col 71}f. Interval]
{hline 17}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
max_person_scale {c |}{col 18}{res}{space 2}  4.67759{col 30}{space 2} .9486596{col 41}{space 1}    4.93{col 50}{space 3}0.000{col 58}{space 4} 2.818251{col 71}{space 3} 6.536928
{txt}{space 11}_cons {c |}{col 18}{res}{space 2}-2.550368{col 30}{space 2} .2311586{col 41}{space 1}  -11.03{col 50}{space 3}0.000{col 58}{space 4} -3.00343{col 71}{space 3}-2.097305
{txt}{hline 17}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a50
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}   -181.729{col 42}{txt}Log-Lik Full Model:{col 69}{res}   -152.113
{txt}D(555):{col 28}{res}    304.226{col 42}{txt}LR(1):{col 69}{res}     59.233
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.000
{txt}McFadden's R2:{col 28}{res}      0.163{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.152
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.101{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.210
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.148{col 42}{txt}Efron's R2:{col 69}{res}      0.195
{txt}Variance of y*:{col 28}{res}      3.862{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.917{col 42}{txt}Adj Count R2:{col 69}{res}      0.179
{txt}AIC:{col 28}{res}      0.553{col 42}{txt}AIC*n:{col 69}{res}    308.226
{txt}BIC:{col 28}{res}  -3204.798{col 42}{txt}BIC':{col 69}{res}    -52.910
{txt}BIC used by Stata:{col 28}{res}    316.871{col 42}{txt}AIC used by Stata:{col 69}{res}    308.226
{txt}
{com}. 
{txt}end of do-file

{com}. do "C:\Users\mtr012\AppData\Local\Temp\STD00000000.tmp"
{txt}
{com}. 
. *Table A20
. 
. logit punish max_person_scale irr_entry  max_both_max_pts previous_sum_punish instit_control if regime==2, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -34.29649}  
Iteration 1:{space 3}log pseudolikelihood = {res:-28.423163}  
Iteration 2:{space 3}log pseudolikelihood = {res:-28.389138}  
Iteration 3:{space 3}log pseudolikelihood = {res:-28.389117}  
Iteration 4:{space 3}log pseudolikelihood = {res:-28.389117}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}        50
{txt}{col 49}Wald chi2({res}5{txt}){col 67}= {res}     11.38
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0444
{txt}Log pseudolikelihood = {res}-28.389117{txt}{col 49}Pseudo R2{col 67}= {res}    0.1722

{txt}{ralign 85:(Std. Err. adjusted for {res:33} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}             punish{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}max_person_scale {c |}{col 21}{res}{space 2} 2.253402{col 33}{space 2} 1.065409{col 44}{space 1}    2.12{col 53}{space 3}0.034{col 61}{space 4}  .165238{col 74}{space 3} 4.341565
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2}-.4104148{col 33}{space 2} 1.049439{col 44}{space 1}   -0.39{col 53}{space 3}0.696{col 61}{space 4}-2.467278{col 74}{space 3} 1.646448
{txt}{space 3}max_both_max_pts {c |}{col 21}{res}{space 2} .6068879{col 33}{space 2}  .427913{col 44}{space 1}    1.42{col 53}{space 3}0.156{col 61}{space 4}-.2318061{col 74}{space 3} 1.445582
{txt}previous_sum_punish {c |}{col 21}{res}{space 2}-.0284245{col 33}{space 2} .0765742{col 44}{space 1}   -0.37{col 53}{space 3}0.710{col 61}{space 4}-.1785072{col 74}{space 3} .1216582
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-1.802896{col 33}{space 2} .8877401{col 44}{space 1}   -2.03{col 53}{space 3}0.042{col 61}{space 4}-3.542834{col 74}{space 3}-.0629569
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-1.442291{col 33}{space 2} 1.374396{col 44}{space 1}   -1.05{col 53}{space 3}0.294{col 61}{space 4}-4.136059{col 74}{space 3} 1.251476
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a51
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}    -34.296{col 42}{txt}Log-Lik Full Model:{col 69}{res}    -28.389
{txt}D(44):{col 28}{res}     56.778{col 42}{txt}LR(5):{col 69}{res}     11.815
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.037
{txt}McFadden's R2:{col 28}{res}      0.172{col 42}{txt}McFadden's Adj R2:{col 69}{res}     -0.003
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.210{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.282
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.320{col 42}{txt}Efron's R2:{col 69}{res}      0.209
{txt}Variance of y*:{col 28}{res}      4.839{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.720{col 42}{txt}Adj Count R2:{col 69}{res}      0.364
{txt}AIC:{col 28}{res}      1.376{col 42}{txt}AIC*n:{col 69}{res}     68.778
{txt}BIC:{col 28}{res}   -115.351{col 42}{txt}BIC':{col 69}{res}      7.745
{txt}BIC used by Stata:{col 28}{res}     80.250{col 42}{txt}AIC used by Stata:{col 69}{res}     68.778
{txt}
{com}. 
. logit punish max_person_scale irr_entry  max_both_max_pts previous_sum_punish instit_control if regime==4, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-27.910194}  
Iteration 1:{space 3}log pseudolikelihood = {res:-24.961497}  
Iteration 2:{space 3}log pseudolikelihood = {res:-24.938433}  
Iteration 3:{space 3}log pseudolikelihood = {res:-24.938429}  
Iteration 4:{space 3}log pseudolikelihood = {res:-24.938429}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}        42
{txt}{col 49}Wald chi2({res}5{txt}){col 67}= {res}      6.30
{txt}{col 49}Prob > chi2{col 67}= {res}    0.2780
{txt}Log pseudolikelihood = {res}-24.938429{txt}{col 49}Pseudo R2{col 67}= {res}    0.1065

{txt}{ralign 85:(Std. Err. adjusted for {res:26} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}             punish{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}max_person_scale {c |}{col 21}{res}{space 2} 2.421759{col 33}{space 2}  1.04771{col 44}{space 1}    2.31{col 53}{space 3}0.021{col 61}{space 4} .3682858{col 74}{space 3} 4.475232
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2}-1.108505{col 33}{space 2} .9945453{col 44}{space 1}   -1.11{col 53}{space 3}0.265{col 61}{space 4}-3.057778{col 74}{space 3} .8407684
{txt}{space 3}max_both_max_pts {c |}{col 21}{res}{space 2}-.1029178{col 33}{space 2} .4834485{col 44}{space 1}   -0.21{col 53}{space 3}0.831{col 61}{space 4}-1.050459{col 74}{space 3} .8446238
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0711758{col 33}{space 2} .0744763{col 44}{space 1}    0.96{col 53}{space 3}0.339{col 61}{space 4}-.0747952{col 74}{space 3} .2171467
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.6953801{col 33}{space 2} .9998739{col 44}{space 1}   -0.70{col 53}{space 3}0.487{col 61}{space 4}-2.655097{col 74}{space 3} 1.264337
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-.1207075{col 33}{space 2} 2.034167{col 44}{space 1}   -0.06{col 53}{space 3}0.953{col 61}{space 4}-4.107601{col 74}{space 3} 3.866186
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a52
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}    -27.910{col 42}{txt}Log-Lik Full Model:{col 69}{res}    -24.938
{txt}D(36):{col 28}{res}     49.877{col 42}{txt}LR(5):{col 69}{res}      5.944
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.312
{txt}McFadden's R2:{col 28}{res}      0.106{col 42}{txt}McFadden's Adj R2:{col 69}{res}     -0.108
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.132{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.179
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.178{col 42}{txt}Efron's R2:{col 69}{res}      0.132
{txt}Variance of y*:{col 28}{res}      4.002{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.667{col 42}{txt}Adj Count R2:{col 69}{res}      0.125
{txt}AIC:{col 28}{res}      1.473{col 42}{txt}AIC*n:{col 69}{res}     61.877
{txt}BIC:{col 28}{res}    -84.679{col 42}{txt}BIC':{col 69}{res}     12.745
{txt}BIC used by Stata:{col 28}{res}     72.303{col 42}{txt}AIC used by Stata:{col 69}{res}     61.877
{txt}
{com}. 
. logit punish max_person_scale irr_entry  max_both_max_pts previous_sum_punish instit_control if regime==5, cluster(ccode)

{txt}note: instit_control omitted because of collinearity
{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-98.935479}  
Iteration 1:{space 3}log pseudolikelihood = {res:-89.726523}  
Iteration 2:{space 3}log pseudolikelihood = {res:-87.513832}  
Iteration 3:{space 3}log pseudolikelihood = {res: -81.59738}  
Iteration 4:{space 3}log pseudolikelihood = {res:-81.110796}  
Iteration 5:{space 3}log pseudolikelihood = {res:-81.109213}  
Iteration 6:{space 3}log pseudolikelihood = {res:-81.109213}  
{res}
{txt}Logistic regression{col 49}Number of obs{col 67}= {res}       338
{txt}{col 49}Wald chi2({res}4{txt}){col 67}= {res}     33.63
{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000
{txt}Log pseudolikelihood = {res}-81.109213{txt}{col 49}Pseudo R2{col 67}= {res}    0.1802

{txt}{ralign 85:(Std. Err. adjusted for {res:79} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}             punish{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 3}max_person_scale {c |}{col 21}{res}{space 2} 2.328444{col 33}{space 2} 1.061177{col 44}{space 1}    2.19{col 53}{space 3}0.028{col 61}{space 4}  .248575{col 74}{space 3} 4.408312
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2}  -1.0236{col 33}{space 2} 1.601378{col 44}{space 1}   -0.64{col 53}{space 3}0.523{col 61}{space 4}-4.162242{col 74}{space 3} 2.115043
{txt}{space 3}max_both_max_pts {c |}{col 21}{res}{space 2} .7488702{col 33}{space 2} .1889615{col 44}{space 1}    3.96{col 53}{space 3}0.000{col 61}{space 4} .3785125{col 74}{space 3} 1.119228
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0437126{col 33}{space 2} .0188372{col 44}{space 1}    2.32{col 53}{space 3}0.020{col 61}{space 4} .0067924{col 74}{space 3} .0806328
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}        0{col 33}{txt}  (omitted)
{space 14}_cons {c |}{col 21}{res}{space 2}-5.180567{col 33}{space 2} .7372404{col 44}{space 1}   -7.03{col 53}{space 3}0.000{col 61}{space 4}-6.625532{col 74}{space 3}-3.735603
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a53
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}logit{txt} of {res}punish

{txt}Log-Lik Intercept Only:{col 28}{res}    -98.935{col 42}{txt}Log-Lik Full Model:{col 69}{res}    -81.109
{txt}D(333):{col 28}{res}    162.218{col 42}{txt}LR(4):{col 69}{res}     35.653
{txt}{col 28}{res}{col 42}{txt}Prob > LR:{col 69}{res}      0.000
{txt}McFadden's R2:{col 28}{res}      0.180{col 42}{txt}McFadden's Adj R2:{col 69}{res}      0.130
{txt}ML (Cox-Snell) R2:{col 28}{res}      0.100{col 42}{txt}Cragg-Uhler(Nagelkerke) R2:{col 69}{res}      0.226
{txt}McKelvey & Zavoina's R2:{col 28}{res}      0.296{col 42}{txt}Efron's R2:{col 69}{res}      0.132
{txt}Variance of y*:{col 28}{res}      4.674{col 42}{txt}Variance of error:{col 69}{res}      3.290
{txt}Count R2:{col 28}{res}      0.914{col 42}{txt}Adj Count R2:{col 69}{res}      0.000
{txt}AIC:{col 28}{res}      0.510{col 42}{txt}AIC*n:{col 69}{res}    172.218
{txt}BIC:{col 28}{res}  -1776.856{col 42}{txt}BIC':{col 69}{res}    -12.360
{txt}BIC used by Stata:{col 28}{res}    191.334{col 42}{txt}AIC used by Stata:{col 69}{res}    172.218
{txt}
{com}. 
{txt}end of do-file

{com}. do "C:\Users\mtr012\AppData\Local\Temp\STD00000000.tmp"
{txt}
{com}. *Table A21
. 
. mprobit posttenurefate gwf_personalist irr_entry max_purges previous_sum_punish  instit_control, base(0) cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-597.02143}  
{res}{txt}Iteration 1:{space 3}log pseudolikelihood = {res:-589.44777}  
{res}{txt}Iteration 2:{space 3}log pseudolikelihood = {res:-589.32756}  
{res}{txt}Iteration 3:{space 3}log pseudolikelihood = {res:-589.32724}  
{res}{txt}Iteration 4:{space 3}log pseudolikelihood = {res:-589.32724}  
{res}
{txt}Multinomial probit regression{col 49}Number of obs{col 67}= {res}       921
{txt}{col 49}Wald chi2({res}15{txt}){col 67}= {res}    116.85
{txt}Log pseudolikelihood = {res}-589.32724{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000

{txt}{ralign 85:(Std. Err. adjusted for {res:125} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}     posttenurefate{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0                  {col 21}{txt}{c |}  (base outcome)
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1                   {txt}{c |}
{space 4}gwf_personalist {c |}{col 21}{res}{space 2} 1.541845{col 33}{space 2} .3326247{col 44}{space 1}    4.64{col 53}{space 3}0.000{col 61}{space 4} .8899129{col 74}{space 3} 2.193778
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2} .3690891{col 33}{space 2} .2456866{col 44}{space 1}    1.50{col 53}{space 3}0.133{col 61}{space 4}-.1124477{col 74}{space 3} .8506259
{txt}{space 9}max_purges {c |}{col 21}{res}{space 2} .1308919{col 33}{space 2} .0808586{col 44}{space 1}    1.62{col 53}{space 3}0.105{col 61}{space 4} -.027588{col 74}{space 3} .2893718
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0592751{col 33}{space 2} .0109945{col 44}{space 1}    5.39{col 53}{space 3}0.000{col 61}{space 4} .0377263{col 74}{space 3}  .080824
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-1.248827{col 33}{space 2} .2613375{col 44}{space 1}   -4.78{col 53}{space 3}0.000{col 61}{space 4}-1.761039{col 74}{space 3}-.7366152
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-1.186598{col 33}{space 2} .2597384{col 44}{space 1}   -4.57{col 53}{space 3}0.000{col 61}{space 4}-1.695676{col 74}{space 3}-.6775197
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}2                   {txt}{c |}
{space 4}gwf_personalist {c |}{col 21}{res}{space 2} 1.214502{col 33}{space 2} .3051504{col 44}{space 1}    3.98{col 53}{space 3}0.000{col 61}{space 4} .6164185{col 74}{space 3} 1.812586
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2} .6156723{col 33}{space 2}   .23531{col 44}{space 1}    2.62{col 53}{space 3}0.009{col 61}{space 4} .1544731{col 74}{space 3} 1.076871
{txt}{space 9}max_purges {c |}{col 21}{res}{space 2} .1018387{col 33}{space 2} .0708292{col 44}{space 1}    1.44{col 53}{space 3}0.150{col 61}{space 4} -.036984{col 74}{space 3} .2406615
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0138941{col 33}{space 2} .0198567{col 44}{space 1}    0.70{col 53}{space 3}0.484{col 61}{space 4}-.0250242{col 74}{space 3} .0528124
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.7014072{col 33}{space 2} .2738475{col 44}{space 1}   -2.56{col 53}{space 3}0.010{col 61}{space 4}-1.238138{col 74}{space 3}-.1646759
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-1.562543{col 33}{space 2} .2768375{col 44}{space 1}   -5.64{col 53}{space 3}0.000{col 61}{space 4}-2.105135{col 74}{space 3}-1.019952
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}3                   {txt}{c |}
{space 4}gwf_personalist {c |}{col 21}{res}{space 2} 1.388301{col 33}{space 2}  .334483{col 44}{space 1}    4.15{col 53}{space 3}0.000{col 61}{space 4} .7327267{col 74}{space 3} 2.043876
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2} .3086405{col 33}{space 2} .2924083{col 44}{space 1}    1.06{col 53}{space 3}0.291{col 61}{space 4}-.2644692{col 74}{space 3} .8817502
{txt}{space 9}max_purges {c |}{col 21}{res}{space 2}  .120864{col 33}{space 2} .0642676{col 44}{space 1}    1.88{col 53}{space 3}0.060{col 61}{space 4}-.0050983{col 74}{space 3} .2468262
{txt}previous_sum_punish {c |}{col 21}{res}{space 2}-.0304313{col 33}{space 2} .0254923{col 44}{space 1}   -1.19{col 53}{space 3}0.233{col 61}{space 4}-.0803952{col 74}{space 3} .0195327
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.9525597{col 33}{space 2} .2973133{col 44}{space 1}   -3.20{col 53}{space 3}0.001{col 61}{space 4}-1.535283{col 74}{space 3}-.3698363
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-1.540793{col 33}{space 2} .2940468{col 44}{space 1}   -5.24{col 53}{space 3}0.000{col 61}{space 4}-2.117114{col 74}{space 3}-.9644721
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a54
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}mprobit{txt} of {res}posttenurefate

{txt}Log-Lik Full Model:{col 28}{res}   -589.327{col 42}{txt}D(903):{col 69}{res}   1178.654
{txt}Wald X2(15):{col 28}{res}    116.850{col 42}{txt}Prob > X2:{col 69}{res}      0.000
{txt}Count R2:{col 28}{res}      0.789{col 42}{txt}Adj Count R2:{col 69}{res}      0.025
{txt}AIC:{col 28}{res}      1.319{col 42}{txt}AIC*n:{col 69}{res}   1214.654
{txt}BIC:{col 28}{res}  -4984.736{col 42}{txt}BIC':{col 69}{res}    -14.468
{txt}BIC used by Stata:{col 28}{res}   1301.513{col 42}{txt}AIC used by Stata:{col 69}{res}   1214.654
{txt}
{com}. 
. mprobit posttenurefate max_pers_magaloni max_military_scale gwf_democracy irr_entry max_purges previous_sum_punish  instit_control, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-380.41927}  
{res}{txt}Iteration 1:{space 3}log pseudolikelihood = {res:  -378.036}  
{res}{txt}Iteration 2:{space 3}log pseudolikelihood = {res:-377.88987}  
{res}{txt}Iteration 3:{space 3}log pseudolikelihood = {res:-377.88945}  
{res}{txt}Iteration 4:{space 3}log pseudolikelihood = {res:-377.88945}  
{res}
{txt}Multinomial probit regression{col 49}Number of obs{col 67}= {res}       721
{txt}{col 49}Wald chi2({res}21{txt}){col 67}= {res}    231.62
{txt}Log pseudolikelihood = {res}-377.88945{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000

{txt}{ralign 85:(Std. Err. adjusted for {res:113} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}     posttenurefate{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0                  {col 21}{txt}{c |}  (base outcome)
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1                   {txt}{c |}
{space 2}max_pers_magaloni {c |}{col 21}{res}{space 2} 1.115311{col 33}{space 2} .2370811{col 44}{space 1}    4.70{col 53}{space 3}0.000{col 61}{space 4} .6506405{col 74}{space 3} 1.579981
{txt}{space 1}max_military_scale {c |}{col 21}{res}{space 2} .6393037{col 33}{space 2} .3756158{col 44}{space 1}    1.70{col 53}{space 3}0.089{col 61}{space 4}-.0968898{col 74}{space 3} 1.375497
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2}-.2197164{col 33}{space 2}  .292687{col 44}{space 1}   -0.75{col 53}{space 3}0.453{col 61}{space 4}-.7933723{col 74}{space 3} .3539396
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2} -.481566{col 33}{space 2} .2953974{col 44}{space 1}   -1.63{col 53}{space 3}0.103{col 61}{space 4}-1.060534{col 74}{space 3} .0974023
{txt}{space 9}max_purges {c |}{col 21}{res}{space 2}  .031503{col 33}{space 2} .0591827{col 44}{space 1}    0.53{col 53}{space 3}0.595{col 61}{space 4}-.0844931{col 74}{space 3}  .147499
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0395506{col 33}{space 2} .0141152{col 44}{space 1}    2.80{col 53}{space 3}0.005{col 61}{space 4} .0118853{col 74}{space 3} .0672159
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.4796975{col 33}{space 2} .3271641{col 44}{space 1}   -1.47{col 53}{space 3}0.143{col 61}{space 4}-1.120927{col 74}{space 3} .1615324
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-2.417445{col 33}{space 2} .3964219{col 44}{space 1}   -6.10{col 53}{space 3}0.000{col 61}{space 4}-3.194417{col 74}{space 3}-1.640472
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}2                   {txt}{c |}
{space 2}max_pers_magaloni {c |}{col 21}{res}{space 2} 1.582814{col 33}{space 2}  .256722{col 44}{space 1}    6.17{col 53}{space 3}0.000{col 61}{space 4} 1.079648{col 74}{space 3}  2.08598
{txt}{space 1}max_military_scale {c |}{col 21}{res}{space 2} .2058034{col 33}{space 2} .4206253{col 44}{space 1}    0.49{col 53}{space 3}0.625{col 61}{space 4}-.6186071{col 74}{space 3} 1.030214
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2} .3269303{col 33}{space 2} .3409183{col 44}{space 1}    0.96{col 53}{space 3}0.338{col 61}{space 4}-.3412573{col 74}{space 3} .9951178
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2}-.4546249{col 33}{space 2} .3423043{col 44}{space 1}   -1.33{col 53}{space 3}0.184{col 61}{space 4}-1.125529{col 74}{space 3} .2162793
{txt}{space 9}max_purges {c |}{col 21}{res}{space 2} -.055343{col 33}{space 2} .0714008{col 44}{space 1}   -0.78{col 53}{space 3}0.438{col 61}{space 4}-.1952861{col 74}{space 3}    .0846
{txt}previous_sum_punish {c |}{col 21}{res}{space 2}-.0014967{col 33}{space 2} .0167085{col 44}{space 1}   -0.09{col 53}{space 3}0.929{col 61}{space 4}-.0342448{col 74}{space 3} .0312513
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2} .0890072{col 33}{space 2} .3868868{col 44}{space 1}    0.23{col 53}{space 3}0.818{col 61}{space 4}-.6692769{col 74}{space 3} .8472913
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-3.208149{col 33}{space 2} .4971513{col 44}{space 1}   -6.45{col 53}{space 3}0.000{col 61}{space 4}-4.182548{col 74}{space 3}-2.233751
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}3                   {txt}{c |}
{space 2}max_pers_magaloni {c |}{col 21}{res}{space 2} 1.263431{col 33}{space 2} .2013632{col 44}{space 1}    6.27{col 53}{space 3}0.000{col 61}{space 4}  .868766{col 74}{space 3} 1.658095
{txt}{space 1}max_military_scale {c |}{col 21}{res}{space 2} .3615361{col 33}{space 2} .4742134{col 44}{space 1}    0.76{col 53}{space 3}0.446{col 61}{space 4} -.567905{col 74}{space 3} 1.290977
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2} .0697095{col 33}{space 2} .3805111{col 44}{space 1}    0.18{col 53}{space 3}0.855{col 61}{space 4}-.6760785{col 74}{space 3} .8154974
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2}-.4678761{col 33}{space 2} .3919318{col 44}{space 1}   -1.19{col 53}{space 3}0.233{col 61}{space 4}-1.236048{col 74}{space 3} .3002961
{txt}{space 9}max_purges {c |}{col 21}{res}{space 2} .0111729{col 33}{space 2} .0476743{col 44}{space 1}    0.23{col 53}{space 3}0.815{col 61}{space 4} -.082267{col 74}{space 3} .1046128
{txt}previous_sum_punish {c |}{col 21}{res}{space 2}-.0363222{col 33}{space 2} .0317899{col 44}{space 1}   -1.14{col 53}{space 3}0.253{col 61}{space 4}-.0986292{col 74}{space 3} .0259847
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.2674541{col 33}{space 2}  .396028{col 44}{space 1}   -0.68{col 53}{space 3}0.499{col 61}{space 4}-1.043655{col 74}{space 3} .5087466
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-2.849829{col 33}{space 2} .4371117{col 44}{space 1}   -6.52{col 53}{space 3}0.000{col 61}{space 4}-3.706553{col 74}{space 3}-1.993106
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a55
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}mprobit{txt} of {res}posttenurefate

{txt}Log-Lik Full Model:{col 28}{res}   -377.889{col 42}{txt}D(697):{col 69}{res}    755.779
{txt}Wald X2(21):{col 28}{res}    231.618{col 42}{txt}Prob > X2:{col 69}{res}      0.000
{txt}Count R2:{col 28}{res}      0.807{col 42}{txt}Adj Count R2:{col 69}{res}      0.054
{txt}AIC:{col 28}{res}      1.115{col 42}{txt}AIC*n:{col 69}{res}    803.779
{txt}BIC:{col 28}{res}  -3830.927{col 42}{txt}BIC':{col 69}{res}    -93.424
{txt}BIC used by Stata:{col 28}{res}    913.714{col 42}{txt}AIC used by Stata:{col 69}{res}    803.779
{txt}
{com}. 
{txt}end of do-file

{com}. do "C:\Users\mtr012\AppData\Local\Temp\STD00000000.tmp"
{txt}
{com}. *Table A22
. 
. mprobit posttenurefate gwf_personalist irr_entry max_both_max_pts previous_sum_punish  instit_control, base(0) cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-299.77951}  
{res}{txt}Iteration 1:{space 3}log pseudolikelihood = {res:-298.73411}  
{res}{txt}Iteration 2:{space 3}log pseudolikelihood = {res:-298.67677}  
{res}{txt}Iteration 3:{space 3}log pseudolikelihood = {res:-298.67659}  
{res}{txt}Iteration 4:{space 3}log pseudolikelihood = {res:-298.67659}  
{res}
{txt}Multinomial probit regression{col 49}Number of obs{col 67}= {res}       540
{txt}{col 49}Wald chi2({res}15{txt}){col 67}= {res}    135.18
{txt}Log pseudolikelihood = {res}-298.67659{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000

{txt}{ralign 85:(Std. Err. adjusted for {res:117} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}     posttenurefate{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0                  {col 21}{txt}{c |}  (base outcome)
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1                   {txt}{c |}
{space 4}gwf_personalist {c |}{col 21}{res}{space 2} 1.314806{col 33}{space 2} .4117913{col 44}{space 1}    3.19{col 53}{space 3}0.001{col 61}{space 4} .5077098{col 74}{space 3} 2.121902
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2} .3999241{col 33}{space 2} .3497467{col 44}{space 1}    1.14{col 53}{space 3}0.253{col 61}{space 4}-.2855669{col 74}{space 3} 1.085415
{txt}{space 3}max_both_max_pts {c |}{col 21}{res}{space 2} .5611824{col 33}{space 2}  .136341{col 44}{space 1}    4.12{col 53}{space 3}0.000{col 61}{space 4} .2939589{col 74}{space 3} .8284058
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0453929{col 33}{space 2} .0126637{col 44}{space 1}    3.58{col 53}{space 3}0.000{col 61}{space 4} .0205724{col 74}{space 3} .0702133
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2} -1.51734{col 33}{space 2} .3567312{col 44}{space 1}   -4.25{col 53}{space 3}0.000{col 61}{space 4} -2.21652{col 74}{space 3}-.8181596
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-2.996759{col 33}{space 2} .5029819{col 44}{space 1}   -5.96{col 53}{space 3}0.000{col 61}{space 4}-3.982585{col 74}{space 3}-2.010932
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}2                   {txt}{c |}
{space 4}gwf_personalist {c |}{col 21}{res}{space 2} .9547024{col 33}{space 2} .4389207{col 44}{space 1}    2.18{col 53}{space 3}0.030{col 61}{space 4} .0944337{col 74}{space 3} 1.814971
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2} .5561905{col 33}{space 2} .3101736{col 44}{space 1}    1.79{col 53}{space 3}0.073{col 61}{space 4}-.0517387{col 74}{space 3}  1.16412
{txt}{space 3}max_both_max_pts {c |}{col 21}{res}{space 2} .4223638{col 33}{space 2} .1096907{col 44}{space 1}    3.85{col 53}{space 3}0.000{col 61}{space 4}  .207374{col 74}{space 3} .6373536
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0031977{col 33}{space 2} .0169502{col 44}{space 1}    0.19{col 53}{space 3}0.850{col 61}{space 4}-.0300241{col 74}{space 3} .0364194
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.8626986{col 33}{space 2} .3725271{col 44}{space 1}   -2.32{col 53}{space 3}0.021{col 61}{space 4}-1.592838{col 74}{space 3} -.132559
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-2.706417{col 33}{space 2} .5338902{col 44}{space 1}   -5.07{col 53}{space 3}0.000{col 61}{space 4}-3.752823{col 74}{space 3}-1.660011
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}3                   {txt}{c |}
{space 4}gwf_personalist {c |}{col 21}{res}{space 2} 1.096579{col 33}{space 2} .4496184{col 44}{space 1}    2.44{col 53}{space 3}0.015{col 61}{space 4} .2153431{col 74}{space 3} 1.977815
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2} .4275403{col 33}{space 2}  .392478{col 44}{space 1}    1.09{col 53}{space 3}0.276{col 61}{space 4}-.3417025{col 74}{space 3} 1.196783
{txt}{space 3}max_both_max_pts {c |}{col 21}{res}{space 2} .4074326{col 33}{space 2} .1293037{col 44}{space 1}    3.15{col 53}{space 3}0.002{col 61}{space 4}  .154002{col 74}{space 3} .6608632
{txt}previous_sum_punish {c |}{col 21}{res}{space 2}-.0302738{col 33}{space 2} .0289115{col 44}{space 1}   -1.05{col 53}{space 3}0.295{col 61}{space 4}-.0869393{col 74}{space 3} .0263917
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.9636033{col 33}{space 2} .4358122{col 44}{space 1}   -2.21{col 53}{space 3}0.027{col 61}{space 4} -1.81778{col 74}{space 3}-.1094271
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-2.806112{col 33}{space 2} .5829892{col 44}{space 1}   -4.81{col 53}{space 3}0.000{col 61}{space 4} -3.94875{col 74}{space 3}-1.663474
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a56
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}mprobit{txt} of {res}posttenurefate

{txt}Log-Lik Full Model:{col 28}{res}   -298.677{col 42}{txt}D(522):{col 69}{res}    597.353
{txt}Wald X2(15):{col 28}{res}    135.182{col 42}{txt}Prob > X2:{col 69}{res}      0.000
{txt}Count R2:{col 28}{res}      0.815{col 42}{txt}Adj Count R2:{col 69}{res}      0.048
{txt}AIC:{col 28}{res}      1.173{col 42}{txt}AIC*n:{col 69}{res}    633.353
{txt}BIC:{col 28}{res}  -2686.846{col 42}{txt}BIC':{col 69}{res}    -40.809
{txt}BIC used by Stata:{col 28}{res}    710.601{col 42}{txt}AIC used by Stata:{col 69}{res}    633.353
{txt}
{com}. 
. mprobit posttenurefate max_pers_magaloni max_military_scale gwf_democracy irr_entry max_both_max_pts previous_sum_punish  instit_control, cluster(ccode)

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-237.11681}  
{res}{txt}Iteration 1:{space 3}log pseudolikelihood = {res:-235.47664}  
{res}{txt}Iteration 2:{space 3}log pseudolikelihood = {res:-235.36375}  
{res}{txt}Iteration 3:{space 3}log pseudolikelihood = {res:-235.36351}  
{res}{txt}Iteration 4:{space 3}log pseudolikelihood = {res:-235.36351}  
{res}
{txt}Multinomial probit regression{col 49}Number of obs{col 67}= {res}       460
{txt}{col 49}Wald chi2({res}21{txt}){col 67}= {res}    131.83
{txt}Log pseudolikelihood = {res}-235.36351{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000

{txt}{ralign 85:(Std. Err. adjusted for {res:108} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 1}     posttenurefate{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}0                  {col 21}{txt}{c |}  (base outcome)
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}1                   {txt}{c |}
{space 2}max_pers_magaloni {c |}{col 21}{res}{space 2}  .718062{col 33}{space 2} .2841416{col 44}{space 1}    2.53{col 53}{space 3}0.011{col 61}{space 4} .1611547{col 74}{space 3} 1.274969
{txt}{space 1}max_military_scale {c |}{col 21}{res}{space 2}  .160506{col 33}{space 2} .4848638{col 44}{space 1}    0.33{col 53}{space 3}0.741{col 61}{space 4}-.7898096{col 74}{space 3} 1.110822
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2}-.4645328{col 33}{space 2} .4065221{col 44}{space 1}   -1.14{col 53}{space 3}0.253{col 61}{space 4}-1.261301{col 74}{space 3} .3322358
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2}-.3412414{col 33}{space 2} .4271914{col 44}{space 1}   -0.80{col 53}{space 3}0.424{col 61}{space 4}-1.178521{col 74}{space 3} .4960383
{txt}{space 3}max_both_max_pts {c |}{col 21}{res}{space 2} .4677432{col 33}{space 2} .1556361{col 44}{space 1}    3.01{col 53}{space 3}0.003{col 61}{space 4} .1627021{col 74}{space 3} .7727843
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0265134{col 33}{space 2} .0167171{col 44}{space 1}    1.59{col 53}{space 3}0.113{col 61}{space 4}-.0062515{col 74}{space 3} .0592783
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-1.073685{col 33}{space 2} .4327787{col 44}{space 1}   -2.48{col 53}{space 3}0.013{col 61}{space 4}-1.921916{col 74}{space 3}-.2254547
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-2.966436{col 33}{space 2}   .66592{col 44}{space 1}   -4.45{col 53}{space 3}0.000{col 61}{space 4}-4.271615{col 74}{space 3}-1.661257
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}2                   {txt}{c |}
{space 2}max_pers_magaloni {c |}{col 21}{res}{space 2} 1.284839{col 33}{space 2} .3053812{col 44}{space 1}    4.21{col 53}{space 3}0.000{col 61}{space 4} .6863028{col 74}{space 3} 1.883375
{txt}{space 1}max_military_scale {c |}{col 21}{res}{space 2} .1829899{col 33}{space 2} .5718304{col 44}{space 1}    0.32{col 53}{space 3}0.749{col 61}{space 4}-.9377771{col 74}{space 3} 1.303757
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2} .2629466{col 33}{space 2} .4430716{col 44}{space 1}    0.59{col 53}{space 3}0.553{col 61}{space 4}-.6054578{col 74}{space 3} 1.131351
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2}-.5201262{col 33}{space 2} .4243699{col 44}{space 1}   -1.23{col 53}{space 3}0.220{col 61}{space 4}-1.351876{col 74}{space 3} .3116236
{txt}{space 3}max_both_max_pts {c |}{col 21}{res}{space 2} .2211335{col 33}{space 2} .1301014{col 44}{space 1}    1.70{col 53}{space 3}0.089{col 61}{space 4}-.0338606{col 74}{space 3} .4761275
{txt}previous_sum_punish {c |}{col 21}{res}{space 2}-.0018363{col 33}{space 2} .0156782{col 44}{space 1}   -0.12{col 53}{space 3}0.907{col 61}{space 4}-.0325649{col 74}{space 3} .0288923
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.4008383{col 33}{space 2} .4534213{col 44}{space 1}   -0.88{col 53}{space 3}0.377{col 61}{space 4}-1.289528{col 74}{space 3}  .487851
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-3.212227{col 33}{space 2} .6530337{col 44}{space 1}   -4.92{col 53}{space 3}0.000{col 61}{space 4} -4.49215{col 74}{space 3}-1.932305
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}3                   {txt}{c |}
{space 2}max_pers_magaloni {c |}{col 21}{res}{space 2} .6653229{col 33}{space 2} .2178546{col 44}{space 1}    3.05{col 53}{space 3}0.002{col 61}{space 4} .2383358{col 74}{space 3}  1.09231
{txt}{space 1}max_military_scale {c |}{col 21}{res}{space 2} .2876009{col 33}{space 2} .5352999{col 44}{space 1}    0.54{col 53}{space 3}0.591{col 61}{space 4}-.7615677{col 74}{space 3} 1.336769
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2}-.6432205{col 33}{space 2} .4133289{col 44}{space 1}   -1.56{col 53}{space 3}0.120{col 61}{space 4} -1.45333{col 74}{space 3} .1668894
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2}-.4249832{col 33}{space 2} .5007399{col 44}{space 1}   -0.85{col 53}{space 3}0.396{col 61}{space 4}-1.406415{col 74}{space 3}  .556449
{txt}{space 3}max_both_max_pts {c |}{col 21}{res}{space 2} .2208987{col 33}{space 2} .1556591{col 44}{space 1}    1.42{col 53}{space 3}0.156{col 61}{space 4}-.0841876{col 74}{space 3}  .525985
{txt}previous_sum_punish {c |}{col 21}{res}{space 2}-.0312652{col 33}{space 2} .0349055{col 44}{space 1}   -0.90{col 53}{space 3}0.370{col 61}{space 4}-.0996788{col 74}{space 3} .0371484
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.4675099{col 33}{space 2} .5266154{col 44}{space 1}   -0.89{col 53}{space 3}0.375{col 61}{space 4}-1.499657{col 74}{space 3} .5646373
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-2.491375{col 33}{space 2} .6437109{col 44}{space 1}   -3.87{col 53}{space 3}0.000{col 61}{space 4}-3.753025{col 74}{space 3}-1.229725
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estimates store a57
{txt}
{com}. fitstat
{res}
{txt}Measures of Fit for {res}mprobit{txt} of {res}posttenurefate

{txt}Log-Lik Full Model:{col 28}{res}   -235.364{col 42}{txt}D(436):{col 69}{res}    470.727
{txt}Wald X2(21):{col 28}{res}    131.833{col 42}{txt}Prob > X2:{col 69}{res}      0.000
{txt}Count R2:{col 28}{res}      0.822{col 42}{txt}Adj Count R2:{col 69}{res}      0.068
{txt}AIC:{col 28}{res}      1.128{col 42}{txt}AIC*n:{col 69}{res}    518.727
{txt}BIC:{col 28}{res}  -2202.488{col 42}{txt}BIC':{col 69}{res}     -3.078
{txt}BIC used by Stata:{col 28}{res}    617.876{col 42}{txt}AIC used by Stata:{col 69}{res}    518.727
{txt}
{com}. 
{txt}end of do-file

{com}. do "C:\Users\mtr012\AppData\Local\Temp\STD00000000.tmp"
{txt}
{com}. *Table A23
. 
. biprobit punish irr_exit gwf_personalist irr_entry  max_purges previous_sum_punish instit_control, cluster(ccode)

{txt}Fitting comparison equation 1:

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-480.65888}  
Iteration 1:{space 3}log pseudolikelihood = {res:-400.88241}  
Iteration 2:{space 3}log pseudolikelihood = {res:-400.01787}  
Iteration 3:{space 3}log pseudolikelihood = {res:-400.01764}  
Iteration 4:{space 3}log pseudolikelihood = {res:-400.01764}  
{res}
{txt}Fitting comparison equation 2:

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-452.14406}  
Iteration 1:{space 3}log pseudolikelihood = {res: -373.5296}  
Iteration 2:{space 3}log pseudolikelihood = {res:-372.78029}  
Iteration 3:{space 3}log pseudolikelihood = {res:-372.78019}  
Iteration 4:{space 3}log pseudolikelihood = {res:-372.78019}  
{res}
{txt}Comparison:    log pseudolikelihood = {res}-772.79783

{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-772.79783}  
Iteration 1:{space 3}log pseudolikelihood = {res:-626.19905}  
Iteration 2:{space 3}log pseudolikelihood = {res:-615.28681}  
Iteration 3:{space 3}log pseudolikelihood = {res:-615.16136}  
Iteration 4:{space 3}log pseudolikelihood = {res:-615.16127}  
Iteration 5:{space 3}log pseudolikelihood = {res:-615.16127}  
{res}
{txt}Bivariate probit regression{col 49}Number of obs{col 67}= {res}       921
{txt}{col 49}Wald chi2({res}10{txt}){col 67}= {res}    131.70
{txt}Log pseudolikelihood = {res}-615.16127{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000

{txt}{ralign 85:(Std. Err. adjusted for {res:125} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}punish              {txt}{c |}
{space 4}gwf_personalist {c |}{col 21}{res}{space 2} 1.083852{col 33}{space 2} .1906653{col 44}{space 1}    5.68{col 53}{space 3}0.000{col 61}{space 4} .7101554{col 74}{space 3} 1.457549
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2} .3778248{col 33}{space 2}  .137117{col 44}{space 1}    2.76{col 53}{space 3}0.006{col 61}{space 4} .1090805{col 74}{space 3} .6465692
{txt}{space 9}max_purges {c |}{col 21}{res}{space 2} .0990654{col 33}{space 2} .0605584{col 44}{space 1}    1.64{col 53}{space 3}0.102{col 61}{space 4}-.0196268{col 74}{space 3} .2177576
{txt}previous_sum_punish {c |}{col 21}{res}{space 2}  .027439{col 33}{space 2}  .007637{col 44}{space 1}    3.59{col 53}{space 3}0.000{col 61}{space 4} .0124707{col 74}{space 3} .0424073
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}  -.78425{col 33}{space 2} .1619296{col 44}{space 1}   -4.84{col 53}{space 3}0.000{col 61}{space 4}-1.101626{col 74}{space 3}-.4668738
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-.4999646{col 33}{space 2} .1622753{col 44}{space 1}   -3.08{col 53}{space 3}0.002{col 61}{space 4}-.8180183{col 74}{space 3}-.1819109
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}irr_exit            {txt}{c |}
{space 4}gwf_personalist {c |}{col 21}{res}{space 2} 1.026965{col 33}{space 2} .1750863{col 44}{space 1}    5.87{col 53}{space 3}0.000{col 61}{space 4} .6838024{col 74}{space 3} 1.370128
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2} .5152895{col 33}{space 2} .1374079{col 44}{space 1}    3.75{col 53}{space 3}0.000{col 61}{space 4} .2459749{col 74}{space 3} .7846041
{txt}{space 9}max_purges {c |}{col 21}{res}{space 2} .0927206{col 33}{space 2} .0570437{col 44}{space 1}    1.63{col 53}{space 3}0.104{col 61}{space 4} -.019083{col 74}{space 3} .2045241
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0096871{col 33}{space 2} .0075785{col 44}{space 1}    1.28{col 53}{space 3}0.201{col 61}{space 4}-.0051665{col 74}{space 3} .0245407
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.8126951{col 33}{space 2} .1596497{col 44}{space 1}   -5.09{col 53}{space 3}0.000{col 61}{space 4}-1.125603{col 74}{space 3}-.4997873
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-.4887121{col 33}{space 2} .1596484{col 44}{space 1}   -3.06{col 53}{space 3}0.002{col 61}{space 4}-.8016171{col 74}{space 3} -.175807
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
            /athrho {c |}{col 21}{res}{space 2} 1.503693{col 33}{space 2} .1144172{col 44}{space 1}   13.14{col 53}{space 3}0.000{col 61}{space 4} 1.279439{col 74}{space 3} 1.727946
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                rho{col 21}{c |}{res}{space 2} .9058133{col 33}{space 2} .0205381{col 61}{space 4} .8563354{col 74}{space 3} .9388128
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Wald test of rho=0: chi2({res}1{txt}) = {res}172.717                     {txt}Prob > chi2 = {res}0.0000
{txt}
{com}. estimates store a58
{txt}
{com}. 
. biprobit punish irr_exit gwf_personalist irr_entry  max_both_max_pts previous_sum_punish instit_control, cluster(ccode)

{txt}Fitting comparison equation 1:

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-266.00598}  
Iteration 1:{space 3}log pseudolikelihood = {res:-197.63811}  
Iteration 2:{space 3}log pseudolikelihood = {res:-196.23457}  
Iteration 3:{space 3}log pseudolikelihood = {res: -196.2319}  
Iteration 4:{space 3}log pseudolikelihood = {res: -196.2319}  
{res}
{txt}Fitting comparison equation 2:

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-244.90584}  
Iteration 1:{space 3}log pseudolikelihood = {res: -186.0816}  
Iteration 2:{space 3}log pseudolikelihood = {res: -185.0456}  
Iteration 3:{space 3}log pseudolikelihood = {res:-185.04313}  
Iteration 4:{space 3}log pseudolikelihood = {res:-185.04313}  
{res}
{txt}Comparison:    log pseudolikelihood = {res}-381.27503

{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-381.27503}  
Iteration 1:{space 3}log pseudolikelihood = {res:-321.86415}  
Iteration 2:{space 3}log pseudolikelihood = {res:-318.75233}  
Iteration 3:{space 3}log pseudolikelihood = {res:-318.72327}  
Iteration 4:{space 3}log pseudolikelihood = {res:-318.72327}  
{res}
{txt}Bivariate probit regression{col 49}Number of obs{col 67}= {res}       540
{txt}{col 49}Wald chi2({res}10{txt}){col 67}= {res}    141.46
{txt}Log pseudolikelihood = {res}-318.72327{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000

{txt}{ralign 85:(Std. Err. adjusted for {res:117} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}punish              {txt}{c |}
{space 4}gwf_personalist {c |}{col 21}{res}{space 2} .8434977{col 33}{space 2} .2561019{col 44}{space 1}    3.29{col 53}{space 3}0.001{col 61}{space 4} .3415472{col 74}{space 3} 1.345448
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2} .4050588{col 33}{space 2}  .190628{col 44}{space 1}    2.12{col 53}{space 3}0.034{col 61}{space 4} .0314348{col 74}{space 3} .7786827
{txt}{space 3}max_both_max_pts {c |}{col 21}{res}{space 2} .3699548{col 33}{space 2} .0641436{col 44}{space 1}    5.77{col 53}{space 3}0.000{col 61}{space 4} .2442356{col 74}{space 3}  .495674
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0161733{col 33}{space 2} .0068303{col 44}{space 1}    2.37{col 53}{space 3}0.018{col 61}{space 4} .0027862{col 74}{space 3} .0295605
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2} -.881276{col 33}{space 2} .2255401{col 44}{space 1}   -3.91{col 53}{space 3}0.000{col 61}{space 4}-1.323326{col 74}{space 3}-.4392255
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-1.634924{col 33}{space 2} .3001375{col 44}{space 1}   -5.45{col 53}{space 3}0.000{col 61}{space 4}-2.223183{col 74}{space 3}-1.046665
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}irr_exit            {txt}{c |}
{space 4}gwf_personalist {c |}{col 21}{res}{space 2} .6669596{col 33}{space 2} .2514352{col 44}{space 1}    2.65{col 53}{space 3}0.008{col 61}{space 4} .1741556{col 74}{space 3} 1.159764
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2} .3947418{col 33}{space 2} .2105553{col 44}{space 1}    1.87{col 53}{space 3}0.061{col 61}{space 4}-.0179391{col 74}{space 3} .8074227
{txt}{space 3}max_both_max_pts {c |}{col 21}{res}{space 2} .3128222{col 33}{space 2} .0744879{col 44}{space 1}    4.20{col 53}{space 3}0.000{col 61}{space 4} .1668287{col 74}{space 3} .4588157
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} -.005441{col 33}{space 2} .0095893{col 44}{space 1}   -0.57{col 53}{space 3}0.570{col 61}{space 4}-.0242356{col 74}{space 3} .0133536
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.9455676{col 33}{space 2} .2178668{col 44}{space 1}   -4.34{col 53}{space 3}0.000{col 61}{space 4}-1.372579{col 74}{space 3}-.5185565
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-1.343817{col 33}{space 2} .3015043{col 44}{space 1}   -4.46{col 53}{space 3}0.000{col 61}{space 4}-1.934754{col 74}{space 3}-.7528789
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
            /athrho {c |}{col 21}{res}{space 2} 1.303237{col 33}{space 2} .1364816{col 44}{space 1}    9.55{col 53}{space 3}0.000{col 61}{space 4} 1.035738{col 74}{space 3} 1.570736
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                rho{col 21}{c |}{res}{space 2} .8625541{col 33}{space 2} .0349394{col 61}{space 4} .7761994{col 74}{space 3} .9171427
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Wald test of rho=0: chi2({res}1{txt}) = {res}91.1797                     {txt}Prob > chi2 = {res}0.0000
{txt}
{com}. estimates store a59
{txt}
{com}. 
{txt}end of do-file

{com}. do "C:\Users\mtr012\AppData\Local\Temp\STD00000000.tmp"
{txt}
{com}. *Table A24
. 
. biprobit punish military_exit max_person_scale max_military_scale gwf_democracy irr_entry  max_purges previous_sum_punish instit_control, cluster(ccode)

{txt}Fitting comparison equation 1:

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-353.92557}  
Iteration 1:{space 3}log pseudolikelihood = {res:-239.65483}  
Iteration 2:{space 3}log pseudolikelihood = {res:-238.46184}  
Iteration 3:{space 3}log pseudolikelihood = {res:-238.46004}  
Iteration 4:{space 3}log pseudolikelihood = {res:-238.46004}  
{res}
{txt}Fitting comparison equation 2:

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-268.65118}  
Iteration 1:{space 3}log pseudolikelihood = {res:-165.09749}  
Iteration 2:{space 3}log pseudolikelihood = {res:-160.57653}  
Iteration 3:{space 3}log pseudolikelihood = {res:-160.54662}  
Iteration 4:{space 3}log pseudolikelihood = {res:-160.54662}  
{res}
{txt}Comparison:    log pseudolikelihood = {res}-399.00666

{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-399.00666}  
Iteration 1:{space 3}log pseudolikelihood = {res:-361.76179}  
Iteration 2:{space 3}log pseudolikelihood = {res:-360.47862}  
Iteration 3:{space 3}log pseudolikelihood = {res: -360.4664}  
Iteration 4:{space 3}log pseudolikelihood = {res:-360.46639}  
{res}
{txt}Bivariate probit regression{col 49}Number of obs{col 67}= {res}       730
{txt}{col 49}Wald chi2({res}14{txt}){col 67}= {res}    237.17
{txt}Log pseudolikelihood = {res}-360.46639{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000

{txt}{ralign 85:(Std. Err. adjusted for {res:113} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}punish              {txt}{c |}
{space 3}max_person_scale {c |}{col 21}{res}{space 2}  1.39875{col 33}{space 2}  .227208{col 44}{space 1}    6.16{col 53}{space 3}0.000{col 61}{space 4} .9534304{col 74}{space 3}  1.84407
{txt}{space 1}max_military_scale {c |}{col 21}{res}{space 2} 1.184152{col 33}{space 2} .1925235{col 44}{space 1}    6.15{col 53}{space 3}0.000{col 61}{space 4} .8068133{col 74}{space 3} 1.561491
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2}-.3457877{col 33}{space 2} .1854017{col 44}{space 1}   -1.87{col 53}{space 3}0.062{col 61}{space 4}-.7091683{col 74}{space 3} .0175929
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2}-.5904457{col 33}{space 2} .2318971{col 44}{space 1}   -2.55{col 53}{space 3}0.011{col 61}{space 4}-1.044956{col 74}{space 3}-.1359357
{txt}{space 9}max_purges {c |}{col 21}{res}{space 2} .0241612{col 33}{space 2} .0335427{col 44}{space 1}    0.72{col 53}{space 3}0.471{col 61}{space 4}-.0415813{col 74}{space 3} .0899036
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0242041{col 33}{space 2}  .007433{col 44}{space 1}    3.26{col 53}{space 3}0.001{col 61}{space 4} .0096357{col 74}{space 3} .0387724
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.1827756{col 33}{space 2} .2020603{col 44}{space 1}   -0.90{col 53}{space 3}0.366{col 61}{space 4}-.5788066{col 74}{space 3} .2132553
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-1.188413{col 33}{space 2}  .238085{col 44}{space 1}   -4.99{col 53}{space 3}0.000{col 61}{space 4}-1.655051{col 74}{space 3}-.7217747
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}military_exit       {txt}{c |}
{space 3}max_person_scale {c |}{col 21}{res}{space 2} .8302702{col 33}{space 2} .2177389{col 44}{space 1}    3.81{col 53}{space 3}0.000{col 61}{space 4} .4035099{col 74}{space 3}  1.25703
{txt}{space 1}max_military_scale {c |}{col 21}{res}{space 2} 2.061113{col 33}{space 2} .2350883{col 44}{space 1}    8.77{col 53}{space 3}0.000{col 61}{space 4} 1.600348{col 74}{space 3} 2.521878
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2} -.239257{col 33}{space 2} .2510704{col 44}{space 1}   -0.95{col 53}{space 3}0.341{col 61}{space 4}-.7313459{col 74}{space 3} .2528319
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2}-.7902527{col 33}{space 2} .2016701{col 44}{space 1}   -3.92{col 53}{space 3}0.000{col 61}{space 4}-1.185519{col 74}{space 3}-.3949866
{txt}{space 9}max_purges {c |}{col 21}{res}{space 2} .0050129{col 33}{space 2} .0320294{col 44}{space 1}    0.16{col 53}{space 3}0.876{col 61}{space 4}-.0577637{col 74}{space 3} .0677894
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0047542{col 33}{space 2}  .009821{col 44}{space 1}    0.48{col 53}{space 3}0.628{col 61}{space 4}-.0144947{col 74}{space 3} .0240031
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.1739234{col 33}{space 2} .2307859{col 44}{space 1}   -0.75{col 53}{space 3}0.451{col 61}{space 4}-.6262554{col 74}{space 3} .2784086
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-1.766075{col 33}{space 2} .3184025{col 44}{space 1}   -5.55{col 53}{space 3}0.000{col 61}{space 4}-2.390132{col 74}{space 3}-1.142018
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
            /athrho {c |}{col 21}{res}{space 2} 1.047328{col 33}{space 2} .1524819{col 44}{space 1}    6.87{col 53}{space 3}0.000{col 61}{space 4} .7484686{col 74}{space 3} 1.346187
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                rho{col 21}{c |}{res}{space 2} .7807653{col 33}{space 2} .0595298{col 61}{space 4} .6342345{col 74}{space 3} .8731503
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Wald test of rho=0: chi2({res}1{txt}) = {res}47.1768                     {txt}Prob > chi2 = {res}0.0000
{txt}
{com}. estimates store a60
{txt}
{com}. 
. biprobit punish military_exit max_person_scale max_military_scale gwf_democracy irr_entry  max_both_max_pts previous_sum_punish instit_control, cluster(ccode)

{txt}Fitting comparison equation 1:

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-214.23719}  
Iteration 1:{space 3}log pseudolikelihood = {res:-150.23018}  
Iteration 2:{space 3}log pseudolikelihood = {res:-148.54319}  
Iteration 3:{space 3}log pseudolikelihood = {res:-148.53551}  
Iteration 4:{space 3}log pseudolikelihood = {res:-148.53551}  
{res}
{txt}Fitting comparison equation 2:

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-142.14309}  
Iteration 1:{space 3}log pseudolikelihood = {res:-87.742505}  
Iteration 2:{space 3}log pseudolikelihood = {res:-84.800026}  
Iteration 3:{space 3}log pseudolikelihood = {res:-84.781468}  
Iteration 4:{space 3}log pseudolikelihood = {res:-84.781465}  
{res}
{txt}Comparison:    log pseudolikelihood = {res}-233.31697

{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-233.31697}  
Iteration 1:{space 3}log pseudolikelihood = {res: -214.5401}  
Iteration 2:{space 3}log pseudolikelihood = {res:-213.74042}  
Iteration 3:{space 3}log pseudolikelihood = {res:-213.73038}  
Iteration 4:{space 3}log pseudolikelihood = {res:-213.73038}  
{res}
{txt}Bivariate probit regression{col 49}Number of obs{col 67}= {res}       453
{txt}{col 49}Wald chi2({res}14{txt}){col 67}= {res}    254.82
{txt}Log pseudolikelihood = {res}-213.73038{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000

{txt}{ralign 85:(Std. Err. adjusted for {res:108} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}punish              {txt}{c |}
{space 3}max_person_scale {c |}{col 21}{res}{space 2} .9447961{col 33}{space 2} .2863704{col 44}{space 1}    3.30{col 53}{space 3}0.001{col 61}{space 4} .3835203{col 74}{space 3} 1.506072
{txt}{space 1}max_military_scale {c |}{col 21}{res}{space 2} .7341585{col 33}{space 2} .2800755{col 44}{space 1}    2.62{col 53}{space 3}0.009{col 61}{space 4} .1852206{col 74}{space 3} 1.283096
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2}-.4909255{col 33}{space 2} .2082884{col 44}{space 1}   -2.36{col 53}{space 3}0.018{col 61}{space 4}-.8991633{col 74}{space 3}-.0826877
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2} -.421554{col 33}{space 2} .2922114{col 44}{space 1}   -1.44{col 53}{space 3}0.149{col 61}{space 4}-.9942778{col 74}{space 3} .1511698
{txt}{space 3}max_both_max_pts {c |}{col 21}{res}{space 2} .2539419{col 33}{space 2} .0847573{col 44}{space 1}    3.00{col 53}{space 3}0.003{col 61}{space 4} .0878206{col 74}{space 3} .4200631
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0113407{col 33}{space 2} .0074406{col 44}{space 1}    1.52{col 53}{space 3}0.127{col 61}{space 4}-.0032427{col 74}{space 3}  .025924
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.4303986{col 33}{space 2} .2556796{col 44}{space 1}   -1.68{col 53}{space 3}0.092{col 61}{space 4}-.9315215{col 74}{space 3} .0707243
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-1.464951{col 33}{space 2} .3788938{col 44}{space 1}   -3.87{col 53}{space 3}0.000{col 61}{space 4}-2.207569{col 74}{space 3}-.7223326
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}military_exit       {txt}{c |}
{space 3}max_person_scale {c |}{col 21}{res}{space 2} .1105865{col 33}{space 2} .2928448{col 44}{space 1}    0.38{col 53}{space 3}0.706{col 61}{space 4}-.4633787{col 74}{space 3} .6845517
{txt}{space 1}max_military_scale {c |}{col 21}{res}{space 2} 2.191125{col 33}{space 2} .3346377{col 44}{space 1}    6.55{col 53}{space 3}0.000{col 61}{space 4} 1.535247{col 74}{space 3} 2.847003
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2}-.1973203{col 33}{space 2} .3682096{col 44}{space 1}   -0.54{col 53}{space 3}0.592{col 61}{space 4}-.9189978{col 74}{space 3} .5243571
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2}-.9179483{col 33}{space 2} .2911263{col 44}{space 1}   -3.15{col 53}{space 3}0.002{col 61}{space 4}-1.488545{col 74}{space 3}-.3473514
{txt}{space 3}max_both_max_pts {c |}{col 21}{res}{space 2} .0285569{col 33}{space 2} .1102007{col 44}{space 1}    0.26{col 53}{space 3}0.796{col 61}{space 4}-.1874325{col 74}{space 3} .2445463
{txt}previous_sum_punish {c |}{col 21}{res}{space 2}-.0004679{col 33}{space 2} .0116997{col 44}{space 1}   -0.04{col 53}{space 3}0.968{col 61}{space 4}-.0233989{col 74}{space 3} .0224631
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.7412786{col 33}{space 2} .2725383{col 44}{space 1}   -2.72{col 53}{space 3}0.007{col 61}{space 4}-1.275444{col 74}{space 3}-.2071133
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-1.360162{col 33}{space 2} .4764343{col 44}{space 1}   -2.85{col 53}{space 3}0.004{col 61}{space 4}-2.293956{col 74}{space 3}-.4263682
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
            /athrho {c |}{col 21}{res}{space 2} .9904511{col 33}{space 2} .1831231{col 44}{space 1}    5.41{col 53}{space 3}0.000{col 61}{space 4} .6315363{col 74}{space 3} 1.349366
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                rho{col 21}{c |}{res}{space 2} .7575546{col 33}{space 2} .0780308{col 61}{space 4} .5591092{col 74}{space 3} .8739035
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Wald test of rho=0: chi2({res}1{txt}) = {res}29.2536                     {txt}Prob > chi2 = {res}0.0000
{txt}
{com}. estimates store a61
{txt}
{com}. 
. biprobit punish military_exit max_pers_magaloni max_military_scale gwf_democracy irr_entry  max_purges previous_sum_punish instit_control, cluster(ccode)

{txt}Fitting comparison equation 1:

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-364.63795}  
Iteration 1:{space 3}log pseudolikelihood = {res:-238.18197}  
Iteration 2:{space 3}log pseudolikelihood = {res:-236.45585}  
Iteration 3:{space 3}log pseudolikelihood = {res:-236.45113}  
Iteration 4:{space 3}log pseudolikelihood = {res:-236.45113}  
{res}
{txt}Fitting comparison equation 2:

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-280.98918}  
Iteration 1:{space 3}log pseudolikelihood = {res:-161.47128}  
Iteration 2:{space 3}log pseudolikelihood = {res:-155.45641}  
Iteration 3:{space 3}log pseudolikelihood = {res:-155.35822}  
Iteration 4:{space 3}log pseudolikelihood = {res:-155.35807}  
Iteration 5:{space 3}log pseudolikelihood = {res:-155.35807}  
{res}
{txt}Comparison:    log pseudolikelihood = {res} -391.8092

{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -391.8092}  
Iteration 1:{space 3}log pseudolikelihood = {res:-356.19767}  
Iteration 2:{space 3}log pseudolikelihood = {res:-355.08331}  
Iteration 3:{space 3}log pseudolikelihood = {res:-355.07137}  
Iteration 4:{space 3}log pseudolikelihood = {res:-355.07136}  
{res}
{txt}Bivariate probit regression{col 49}Number of obs{col 67}= {res}       721
{txt}{col 49}Wald chi2({res}14{txt}){col 67}= {res}    274.95
{txt}Log pseudolikelihood = {res}-355.07136{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000

{txt}{ralign 85:(Std. Err. adjusted for {res:113} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}punish              {txt}{c |}
{space 2}max_pers_magaloni {c |}{col 21}{res}{space 2} 1.037017{col 33}{space 2} .1404674{col 44}{space 1}    7.38{col 53}{space 3}0.000{col 61}{space 4} .7617063{col 74}{space 3} 1.312328
{txt}{space 1}max_military_scale {c |}{col 21}{res}{space 2} .3130116{col 33}{space 2} .2503049{col 44}{space 1}    1.25{col 53}{space 3}0.211{col 61}{space 4} -.177577{col 74}{space 3} .8036002
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2} .0307133{col 33}{space 2} .1893828{col 44}{space 1}    0.16{col 53}{space 3}0.871{col 61}{space 4}-.3404702{col 74}{space 3} .4018968
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2}-.3572259{col 33}{space 2} .2031038{col 44}{space 1}   -1.76{col 53}{space 3}0.079{col 61}{space 4}-.7553021{col 74}{space 3} .0408503
{txt}{space 9}max_purges {c |}{col 21}{res}{space 2} .0076614{col 33}{space 2} .0401342{col 44}{space 1}    0.19{col 53}{space 3}0.849{col 61}{space 4}-.0710003{col 74}{space 3} .0863231
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0114855{col 33}{space 2} .0085282{col 44}{space 1}    1.35{col 53}{space 3}0.178{col 61}{space 4}-.0052294{col 74}{space 3} .0282004
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.2022901{col 33}{space 2} .2207062{col 44}{space 1}   -0.92{col 53}{space 3}0.359{col 61}{space 4}-.6348663{col 74}{space 3} .2302862
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-1.543104{col 33}{space 2} .2561716{col 44}{space 1}   -6.02{col 53}{space 3}0.000{col 61}{space 4}-2.045191{col 74}{space 3}-1.041017
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}military_exit       {txt}{c |}
{space 2}max_pers_magaloni {c |}{col 21}{res}{space 2} .9380888{col 33}{space 2} .1513658{col 44}{space 1}    6.20{col 53}{space 3}0.000{col 61}{space 4} .6414172{col 74}{space 3}  1.23476
{txt}{space 1}max_military_scale {c |}{col 21}{res}{space 2} 1.265174{col 33}{space 2} .3217357{col 44}{space 1}    3.93{col 53}{space 3}0.000{col 61}{space 4} .6345838{col 74}{space 3} 1.895765
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2} .1971284{col 33}{space 2} .2566829{col 44}{space 1}    0.77{col 53}{space 3}0.442{col 61}{space 4}-.3059608{col 74}{space 3} .7002176
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2}-.5759314{col 33}{space 2} .2130143{col 44}{space 1}   -2.70{col 53}{space 3}0.007{col 61}{space 4}-.9934317{col 74}{space 3}-.1584311
{txt}{space 9}max_purges {c |}{col 21}{res}{space 2} .0013423{col 33}{space 2} .0380839{col 44}{space 1}    0.04{col 53}{space 3}0.972{col 61}{space 4}-.0733007{col 74}{space 3} .0759853
{txt}previous_sum_punish {c |}{col 21}{res}{space 2}-.0034355{col 33}{space 2} .0124828{col 44}{space 1}   -0.28{col 53}{space 3}0.783{col 61}{space 4}-.0279014{col 74}{space 3} .0210303
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.1544646{col 33}{space 2} .2316632{col 44}{space 1}   -0.67{col 53}{space 3}0.505{col 61}{space 4}-.6085161{col 74}{space 3} .2995868
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-2.345863{col 33}{space 2} .3016054{col 44}{space 1}   -7.78{col 53}{space 3}0.000{col 61}{space 4}-2.936998{col 74}{space 3}-1.754727
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
            /athrho {c |}{col 21}{res}{space 2} 1.026437{col 33}{space 2} .1601498{col 44}{space 1}    6.41{col 53}{space 3}0.000{col 61}{space 4} .7125496{col 74}{space 3} 1.340325
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                rho{col 21}{c |}{res}{space 2} .7724756{col 33}{space 2} .0645855{col 61}{space 4} .6122731{col 74}{space 3} .8717504
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Wald test of rho=0: chi2({res}1{txt}) = {res}41.0783                     {txt}Prob > chi2 = {res}0.0000
{txt}
{com}. estimates store a62
{txt}
{com}. 
. biprobit punish military_exit max_pers_magaloni max_military_scale gwf_democracy irr_entry  max_both_max_pts previous_sum_punish instit_control, cluster(ccode)

{txt}Fitting comparison equation 1:

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-224.53003}  
Iteration 1:{space 3}log pseudolikelihood = {res:-152.72366}  
Iteration 2:{space 3}log pseudolikelihood = {res:-150.82955}  
Iteration 3:{space 3}log pseudolikelihood = {res:-150.81954}  
Iteration 4:{space 3}log pseudolikelihood = {res:-150.81954}  
{res}
{txt}Fitting comparison equation 2:

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-151.72339}  
Iteration 1:{space 3}log pseudolikelihood = {res:-86.624002}  
Iteration 2:{space 3}log pseudolikelihood = {res:-82.437819}  
Iteration 3:{space 3}log pseudolikelihood = {res:-82.349578}  
Iteration 4:{space 3}log pseudolikelihood = {res:-82.349331}  
Iteration 5:{space 3}log pseudolikelihood = {res:-82.349331}  
{res}
{txt}Comparison:    log pseudolikelihood = {res}-233.16887

{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-233.16887}  
Iteration 1:{space 3}log pseudolikelihood = {res:-212.77481}  
Iteration 2:{space 3}log pseudolikelihood = {res:-211.91244}  
Iteration 3:{space 3}log pseudolikelihood = {res:-211.89682}  
Iteration 4:{space 3}log pseudolikelihood = {res: -211.8968}  
{res}
{txt}Bivariate probit regression{col 49}Number of obs{col 67}= {res}       460
{txt}{col 49}Wald chi2({res}14{txt}){col 67}= {res}    243.88
{txt}Log pseudolikelihood = {res} -211.8968{txt}{col 49}Prob > chi2{col 67}= {res}    0.0000

{txt}{ralign 85:(Std. Err. adjusted for {res:108} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}punish              {txt}{c |}
{space 2}max_pers_magaloni {c |}{col 21}{res}{space 2} .7613979{col 33}{space 2} .1821565{col 44}{space 1}    4.18{col 53}{space 3}0.000{col 61}{space 4} .4043776{col 74}{space 3} 1.118418
{txt}{space 1}max_military_scale {c |}{col 21}{res}{space 2} .1515369{col 33}{space 2} .3283502{col 44}{space 1}    0.46{col 53}{space 3}0.644{col 61}{space 4}-.4920176{col 74}{space 3} .7950915
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2}-.1369128{col 33}{space 2} .2456284{col 44}{space 1}   -0.56{col 53}{space 3}0.577{col 61}{space 4}-.6183356{col 74}{space 3} .3445101
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2}-.3331521{col 33}{space 2} .2628777{col 44}{space 1}   -1.27{col 53}{space 3}0.205{col 61}{space 4} -.848383{col 74}{space 3} .1820787
{txt}{space 3}max_both_max_pts {c |}{col 21}{res}{space 2} .2351607{col 33}{space 2} .0792007{col 44}{space 1}    2.97{col 53}{space 3}0.003{col 61}{space 4} .0799302{col 74}{space 3} .3903913
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0047709{col 33}{space 2} .0080756{col 44}{space 1}    0.59{col 53}{space 3}0.555{col 61}{space 4}-.0110569{col 74}{space 3} .0205988
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2} -.514627{col 33}{space 2} .2740256{col 44}{space 1}   -1.88{col 53}{space 3}0.060{col 61}{space 4}-1.051707{col 74}{space 3} .0224533
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-1.679598{col 33}{space 2} .3848943{col 44}{space 1}   -4.36{col 53}{space 3}0.000{col 61}{space 4}-2.433977{col 74}{space 3}-.9252187
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}military_exit       {txt}{c |}
{space 2}max_pers_magaloni {c |}{col 21}{res}{space 2} .6873383{col 33}{space 2} .2140971{col 44}{space 1}    3.21{col 53}{space 3}0.001{col 61}{space 4} .2677157{col 74}{space 3} 1.106961
{txt}{space 1}max_military_scale {c |}{col 21}{res}{space 2} 1.894492{col 33}{space 2} .3956512{col 44}{space 1}    4.79{col 53}{space 3}0.000{col 61}{space 4}  1.11903{col 74}{space 3} 2.669954
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2} .4011515{col 33}{space 2} .3254633{col 44}{space 1}    1.23{col 53}{space 3}0.218{col 61}{space 4}-.2367448{col 74}{space 3} 1.039048
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2}-.8377992{col 33}{space 2} .2990949{col 44}{space 1}   -2.80{col 53}{space 3}0.005{col 61}{space 4}-1.424014{col 74}{space 3}-.2515841
{txt}{space 3}max_both_max_pts {c |}{col 21}{res}{space 2}-.0928046{col 33}{space 2} .1105956{col 44}{space 1}   -0.84{col 53}{space 3}0.401{col 61}{space 4}-.3095679{col 74}{space 3} .1239587
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0013255{col 33}{space 2} .0136818{col 44}{space 1}    0.10{col 53}{space 3}0.923{col 61}{space 4}-.0254902{col 74}{space 3} .0281413
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.7418404{col 33}{space 2} .2825614{col 44}{space 1}   -2.63{col 53}{space 3}0.009{col 61}{space 4}-1.295651{col 74}{space 3}-.1880302
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-1.811371{col 33}{space 2} .4527466{col 44}{space 1}   -4.00{col 53}{space 3}0.000{col 61}{space 4}-2.698738{col 74}{space 3}-.9240037
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
            /athrho {c |}{col 21}{res}{space 2} 1.100843{col 33}{space 2} .2120052{col 44}{space 1}    5.19{col 53}{space 3}0.000{col 61}{space 4} .6853206{col 74}{space 3} 1.516366
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                rho{col 21}{c |}{res}{space 2} .8008017{col 33}{space 2} .0760498{col 61}{space 4} .5949675{col 74}{space 3} .9080622
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Wald test of rho=0: chi2({res}1{txt}) = {res}26.9624                     {txt}Prob > chi2 = {res}0.0000
{txt}
{com}. estimates store a63
{txt}
{com}. 
{txt}end of do-file

{com}. use "F:\Mussolini\FPA RnR v2\Data\Radtke_FPA_Yearly.dta", clear
{txt}(Archigos  (Time Varying): A Data Set of Political Leaders)

{com}. do "C:\Users\mtr012\AppData\Local\Temp\STD00000000.tmp"
{txt}
{com}. heckprobit punish gwf_party gwf_military gwf_monarch gwf_democracy purges previous_sum_punish instit_control irr_entry, sel(fail=gwf_party gwf_military gwf_monarch gwf_democracy mid civ_war log_pop log_gdppc gdpgrowth prevtimesinoffice age failyears fy2 fy3) cluster(ccode)

{txt}Fitting probit model:

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-392.36898}  
Iteration 1:{space 3}log pseudolikelihood = {res:-304.42092}  
Iteration 2:{space 3}log pseudolikelihood = {res:-303.51304}  
Iteration 3:{space 3}log pseudolikelihood = {res:-303.51258}  
Iteration 4:{space 3}log pseudolikelihood = {res:-303.51258}  
{res}
{txt}Fitting selection model:

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-2340.0716}  
Iteration 1:{space 3}log pseudolikelihood = {res:-2135.1575}  
Iteration 2:{space 3}log pseudolikelihood = {res:-2130.5546}  
Iteration 3:{space 3}log pseudolikelihood = {res:-2130.5442}  
Iteration 4:{space 3}log pseudolikelihood = {res:-2130.5442}  
{res}
{txt}Fitting starting values:

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-542.73424}  
Iteration 1:{space 3}log pseudolikelihood = {res:-304.62195}  
Iteration 2:{space 3}log pseudolikelihood = {res:-303.05359}  
Iteration 3:{space 3}log pseudolikelihood = {res:-303.05297}  
Iteration 4:{space 3}log pseudolikelihood = {res:-303.05297}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-2435.3177}  
Iteration 1:{space 3}log pseudolikelihood = {res:-2433.7364}  
Iteration 2:{space 3}log pseudolikelihood = {res: -2433.681}  
Iteration 3:{space 3}log pseudolikelihood = {res:-2433.6719}  
Iteration 4:{space 3}log pseudolikelihood = {res:-2433.6718}  
{res}
{txt}Probit model with sample selection              Number of obs     = {res}     6,116
{txt}                                                Censored obs      = {res}     5,333
                                                {txt}Uncensored obs    = {res}       783

                                                {txt}Wald chi2({res}8{txt})      =  {res}    43.82
{txt}Log pseudolikelihood = {res}-2433.672                {txt}Prob > chi2       =     {res}0.0000

{txt}{ralign 85:(Std. Err. adjusted for {res:147} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}punish              {txt}{c |}
{space 10}gwf_party {c |}{col 21}{res}{space 2}-.7242415{col 33}{space 2} .2556975{col 44}{space 1}   -2.83{col 53}{space 3}0.005{col 61}{space 4}-1.225399{col 74}{space 3}-.2230836
{txt}{space 7}gwf_military {c |}{col 21}{res}{space 2}-.7665395{col 33}{space 2} .3830817{col 44}{space 1}   -2.00{col 53}{space 3}0.045{col 61}{space 4}-1.517366{col 74}{space 3}-.0157132
{txt}{space 8}gwf_monarch {c |}{col 21}{res}{space 2}  -.65667{col 33}{space 2} .3394237{col 44}{space 1}   -1.93{col 53}{space 3}0.053{col 61}{space 4}-1.321928{col 74}{space 3} .0085881
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2}-1.258078{col 33}{space 2} .4838123{col 44}{space 1}   -2.60{col 53}{space 3}0.009{col 61}{space 4}-2.206333{col 74}{space 3}-.3098233
{txt}{space 13}purges {c |}{col 21}{res}{space 2} .3138781{col 33}{space 2} .0815441{col 44}{space 1}    3.85{col 53}{space 3}0.000{col 61}{space 4} .1540545{col 74}{space 3} .4737016
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0362315{col 33}{space 2} .0096077{col 44}{space 1}    3.77{col 53}{space 3}0.000{col 61}{space 4} .0174007{col 74}{space 3} .0550623
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.2185112{col 33}{space 2} .2054144{col 44}{space 1}   -1.06{col 53}{space 3}0.287{col 61}{space 4} -.621116{col 74}{space 3} .1840937
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2} .0972207{col 33}{space 2} .1709765{col 44}{space 1}    0.57{col 53}{space 3}0.570{col 61}{space 4}-.2378871{col 74}{space 3} .4323285
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-.5135104{col 33}{space 2} .7311044{col 44}{space 1}   -0.70{col 53}{space 3}0.482{col 61}{space 4}-1.946449{col 74}{space 3} .9194278
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}fail                {txt}{c |}
{space 10}gwf_party {c |}{col 21}{res}{space 2} -.012485{col 33}{space 2}   .08325{col 44}{space 1}   -0.15{col 53}{space 3}0.881{col 61}{space 4} -.175652{col 74}{space 3}  .150682
{txt}{space 7}gwf_military {c |}{col 21}{res}{space 2} .5035031{col 33}{space 2} .0983571{col 44}{space 1}    5.12{col 53}{space 3}0.000{col 61}{space 4} .3107267{col 74}{space 3} .6962795
{txt}{space 8}gwf_monarch {c |}{col 21}{res}{space 2}-.1019182{col 33}{space 2} .1039184{col 44}{space 1}   -0.98{col 53}{space 3}0.327{col 61}{space 4}-.3055945{col 74}{space 3} .1017581
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2} .9761043{col 33}{space 2} .0827535{col 44}{space 1}   11.80{col 53}{space 3}0.000{col 61}{space 4} .8139105{col 74}{space 3} 1.138298
{txt}{space 16}mid {c |}{col 21}{res}{space 2} -.010772{col 33}{space 2} .0439903{col 44}{space 1}   -0.24{col 53}{space 3}0.807{col 61}{space 4}-.0969914{col 74}{space 3} .0754475
{txt}{space 12}civ_war {c |}{col 21}{res}{space 2} .0685231{col 33}{space 2} .0768324{col 44}{space 1}    0.89{col 53}{space 3}0.372{col 61}{space 4}-.0820655{col 74}{space 3} .2191117
{txt}{space 12}log_pop {c |}{col 21}{res}{space 2}  .019584{col 33}{space 2} .0148881{col 44}{space 1}    1.32{col 53}{space 3}0.188{col 61}{space 4} -.009596{col 74}{space 3} .0487641
{txt}{space 10}log_gdppc {c |}{col 21}{res}{space 2}-.0506849{col 33}{space 2} .0206452{col 44}{space 1}   -2.46{col 53}{space 3}0.014{col 61}{space 4}-.0911487{col 74}{space 3} -.010221
{txt}{space 10}gdpgrowth {c |}{col 21}{res}{space 2} .0117692{col 33}{space 2} .1778127{col 44}{space 1}    0.07{col 53}{space 3}0.947{col 61}{space 4}-.3367373{col 74}{space 3} .3602756
{txt}{space 2}prevtimesinoffice {c |}{col 21}{res}{space 2}-.0491593{col 33}{space 2} .0494257{col 44}{space 1}   -0.99{col 53}{space 3}0.320{col 61}{space 4}-.1460319{col 74}{space 3} .0477134
{txt}{space 16}age {c |}{col 21}{res}{space 2} .0038646{col 33}{space 2} .0019752{col 44}{space 1}    1.96{col 53}{space 3}0.050{col 61}{space 4}-6.59e-06{col 74}{space 3} .0077359
{txt}{space 10}failyears {c |}{col 21}{res}{space 2} .0796759{col 33}{space 2} .0215823{col 44}{space 1}    3.69{col 53}{space 3}0.000{col 61}{space 4} .0373753{col 74}{space 3} .1219764
{txt}{space 16}fy2 {c |}{col 21}{res}{space 2}-.0047177{col 33}{space 2} .0014847{col 44}{space 1}   -3.18{col 53}{space 3}0.001{col 61}{space 4}-.0076277{col 74}{space 3}-.0018077
{txt}{space 16}fy3 {c |}{col 21}{res}{space 2} .0000814{col 33}{space 2} .0000267{col 44}{space 1}    3.06{col 53}{space 3}0.002{col 61}{space 4} .0000292{col 74}{space 3} .0001337
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-1.903215{col 33}{space 2}  .200708{col 44}{space 1}   -9.48{col 53}{space 3}0.000{col 61}{space 4}-2.296595{col 74}{space 3}-1.509835
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
            /athrho {c |}{col 21}{res}{space 2} .3716653{col 33}{space 2} .3643705{col 44}{space 1}    1.02{col 53}{space 3}0.308{col 61}{space 4}-.3424878{col 74}{space 3} 1.085818
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                rho{col 21}{c |}{res}{space 2} .3554475{col 33}{space 2} .3183349{col 61}{space 4}-.3296966{col 74}{space 3} .7953468
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Wald test of indep. eqns. (rho = 0): chi2({res}1{txt}) = {res}    1.04   {txt}Prob > chi2 = {res}0.3077
{txt}
{com}. estimates store a64
{txt}
{com}. 
. heckprobit punish gwf_party gwf_military gwf_monarch gwf_democracy  both_max_pts previous_sum_punish instit_control irr_entry, sel(fail=gwf_party gwf_military gwf_monarch gwf_democracy mid civ_war log_pop log_gdppc gdpgrowth prevtimesinoffice age failyears fy2 fy3) cluster(ccode)

{txt}Fitting probit model:

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-206.51362}  
Iteration 1:{space 3}log pseudolikelihood = {res:-145.14834}  
Iteration 2:{space 3}log pseudolikelihood = {res:-142.95202}  
Iteration 3:{space 3}log pseudolikelihood = {res:-142.94421}  
Iteration 4:{space 3}log pseudolikelihood = {res: -142.9442}  
{res}
{txt}Fitting selection model:

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1547.6932}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1390.9646}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1382.3596}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1382.3194}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1382.3194}  
{res}
{txt}Fitting starting values:

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-302.90532}  
Iteration 1:{space 3}log pseudolikelihood = {res:-145.76735}  
Iteration 2:{space 3}log pseudolikelihood = {res:-142.38987}  
Iteration 3:{space 3}log pseudolikelihood = {res:-142.35748}  
Iteration 4:{space 3}log pseudolikelihood = {res:-142.35747}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1525.7655}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-1524.9263}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1524.6139}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1524.6126}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1524.6126}  
{res}
{txt}Probit model with sample selection              Number of obs     = {res}     5,770
{txt}                                                Censored obs      = {res}     5,333
                                                {txt}Uncensored obs    = {res}       437

                                                {txt}Wald chi2({res}8{txt})      =  {res}   135.73
{txt}Log pseudolikelihood = {res}-1524.613                {txt}Prob > chi2       =     {res}0.0000

{txt}{ralign 85:(Std. Err. adjusted for {res:147} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}punish              {txt}{c |}
{space 10}gwf_party {c |}{col 21}{res}{space 2}-1.058538{col 33}{space 2} .3258382{col 44}{space 1}   -3.25{col 53}{space 3}0.001{col 61}{space 4}-1.697169{col 74}{space 3}-.4199067
{txt}{space 7}gwf_military {c |}{col 21}{res}{space 2}-1.318094{col 33}{space 2} .3214035{col 44}{space 1}   -4.10{col 53}{space 3}0.000{col 61}{space 4}-1.948033{col 74}{space 3}-.6881543
{txt}{space 8}gwf_monarch {c |}{col 21}{res}{space 2}-1.751939{col 33}{space 2} .6427508{col 44}{space 1}   -2.73{col 53}{space 3}0.006{col 61}{space 4}-3.011708{col 74}{space 3}-.4921709
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2}-1.977316{col 33}{space 2} .3074289{col 44}{space 1}   -6.43{col 53}{space 3}0.000{col 61}{space 4}-2.579866{col 74}{space 3}-1.374767
{txt}{space 7}both_max_pts {c |}{col 21}{res}{space 2} .2795449{col 33}{space 2} .0717545{col 44}{space 1}    3.90{col 53}{space 3}0.000{col 61}{space 4} .1389087{col 74}{space 3} .4201811
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0296241{col 33}{space 2} .0091159{col 44}{space 1}    3.25{col 53}{space 3}0.001{col 61}{space 4} .0117574{col 74}{space 3} .0474909
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.6467343{col 33}{space 2} .2870444{col 44}{space 1}   -2.25{col 53}{space 3}0.024{col 61}{space 4}-1.209331{col 74}{space 3}-.0841375
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2} .1646236{col 33}{space 2}  .259477{col 44}{space 1}    0.63{col 53}{space 3}0.526{col 61}{space 4}-.3439419{col 74}{space 3} .6731892
{txt}{space 14}_cons {c |}{col 21}{res}{space 2} 1.052932{col 33}{space 2}  .879722{col 44}{space 1}    1.20{col 53}{space 3}0.231{col 61}{space 4} -.671291{col 74}{space 3} 2.777156
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}fail                {txt}{c |}
{space 10}gwf_party {c |}{col 21}{res}{space 2}  .164475{col 33}{space 2}  .113072{col 44}{space 1}    1.45{col 53}{space 3}0.146{col 61}{space 4} -.057142{col 74}{space 3}  .386092
{txt}{space 7}gwf_military {c |}{col 21}{res}{space 2} .6450836{col 33}{space 2} .1302275{col 44}{space 1}    4.95{col 53}{space 3}0.000{col 61}{space 4} .3898423{col 74}{space 3} .9003249
{txt}{space 8}gwf_monarch {c |}{col 21}{res}{space 2}-.2540722{col 33}{space 2} .2014329{col 44}{space 1}   -1.26{col 53}{space 3}0.207{col 61}{space 4}-.6488734{col 74}{space 3}  .140729
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2} 1.050442{col 33}{space 2} .1095221{col 44}{space 1}    9.59{col 53}{space 3}0.000{col 61}{space 4} .8357823{col 74}{space 3} 1.265101
{txt}{space 16}mid {c |}{col 21}{res}{space 2}-.0039428{col 33}{space 2}   .05441{col 44}{space 1}   -0.07{col 53}{space 3}0.942{col 61}{space 4}-.1105844{col 74}{space 3} .1026988
{txt}{space 12}civ_war {c |}{col 21}{res}{space 2} .3063784{col 33}{space 2} .0873041{col 44}{space 1}    3.51{col 53}{space 3}0.000{col 61}{space 4} .1352656{col 74}{space 3} .4774912
{txt}{space 12}log_pop {c |}{col 21}{res}{space 2} .0116448{col 33}{space 2} .0199376{col 44}{space 1}    0.58{col 53}{space 3}0.559{col 61}{space 4}-.0274322{col 74}{space 3} .0507217
{txt}{space 10}log_gdppc {c |}{col 21}{res}{space 2} .1478473{col 33}{space 2} .0172976{col 44}{space 1}    8.55{col 53}{space 3}0.000{col 61}{space 4} .1139446{col 74}{space 3} .1817499
{txt}{space 10}gdpgrowth {c |}{col 21}{res}{space 2}-.1043016{col 33}{space 2} .2168769{col 44}{space 1}   -0.48{col 53}{space 3}0.631{col 61}{space 4}-.5293725{col 74}{space 3} .3207693
{txt}{space 2}prevtimesinoffice {c |}{col 21}{res}{space 2} -.101063{col 33}{space 2} .0706541{col 44}{space 1}   -1.43{col 53}{space 3}0.153{col 61}{space 4}-.2395425{col 74}{space 3} .0374164
{txt}{space 16}age {c |}{col 21}{res}{space 2}  .001542{col 33}{space 2} .0025806{col 44}{space 1}    0.60{col 53}{space 3}0.550{col 61}{space 4}-.0035158{col 74}{space 3} .0065999
{txt}{space 10}failyears {c |}{col 21}{res}{space 2} .0662212{col 33}{space 2} .0247426{col 44}{space 1}    2.68{col 53}{space 3}0.007{col 61}{space 4} .0177267{col 74}{space 3} .1147157
{txt}{space 16}fy2 {c |}{col 21}{res}{space 2}-.0027383{col 33}{space 2} .0017425{col 44}{space 1}   -1.57{col 53}{space 3}0.116{col 61}{space 4}-.0061535{col 74}{space 3} .0006768
{txt}{space 16}fy3 {c |}{col 21}{res}{space 2} .0000353{col 33}{space 2} .0000315{col 44}{space 1}    1.12{col 53}{space 3}0.263{col 61}{space 4}-.0000265{col 74}{space 3} .0000971
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-3.613197{col 33}{space 2} .2890144{col 44}{space 1}  -12.50{col 53}{space 3}0.000{col 61}{space 4}-4.179655{col 74}{space 3} -3.04674
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
            /athrho {c |}{col 21}{res}{space 2}-.5189105{col 33}{space 2} .4608798{col 44}{space 1}   -1.13{col 53}{space 3}0.260{col 61}{space 4}-1.422218{col 74}{space 3} .3843973
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                rho{col 21}{c |}{res}{space 2}-.4768587{col 33}{space 2} .3560784{col 61}{space 4}-.8900608{col 74}{space 3} .3665202
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Wald test of indep. eqns. (rho = 0): chi2({res}1{txt}) = {res}    1.27   {txt}Prob > chi2 = {res}0.2602
{txt}
{com}. estimates store a65
{txt}
{com}. 
. heckprobit punish gwf_party gwf_military gwf_monarch gwf_democracy  both_max_pts previous_sum_punish instit_control irr_entry irr_exit, sel(fail=gwf_party gwf_military gwf_monarch gwf_democracy mid civ_war log_pop log_gdppc gdpgrowth prevtimesinoffice age failyears fy2 fy3) cluster(ccode)

{txt}Fitting probit model:

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-206.51362}  
Iteration 1:{space 3}log pseudolikelihood = {res:-99.369291}  
Iteration 2:{space 3}log pseudolikelihood = {res:-96.363418}  
Iteration 3:{space 3}log pseudolikelihood = {res:-96.307636}  
Iteration 4:{space 3}log pseudolikelihood = {res:-96.307603}  
Iteration 5:{space 3}log pseudolikelihood = {res:-96.307603}  
{res}
{txt}Fitting selection model:

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1547.6932}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1390.9646}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1382.3596}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1382.3194}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1382.3194}  
{res}
{txt}Fitting starting values:

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-302.90532}  
Iteration 1:{space 3}log pseudolikelihood = {res:-102.23533}  
Iteration 2:{space 3}log pseudolikelihood = {res:-95.989651}  
Iteration 3:{space 3}log pseudolikelihood = {res:-95.835917}  
Iteration 4:{space 3}log pseudolikelihood = {res:-95.835867}  
Iteration 5:{space 3}log pseudolikelihood = {res:-95.835867}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1479.7158}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-1478.7158}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1478.0653}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1478.0621}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1478.0619}  
Iteration 5:{space 3}log pseudolikelihood = {res:-1478.0619}  
{res}
{txt}Probit model with sample selection              Number of obs     = {res}     5,770
{txt}                                                Censored obs      = {res}     5,333
                                                {txt}Uncensored obs    = {res}       437

                                                {txt}Wald chi2({res}9{txt})      =  {res}    82.34
{txt}Log pseudolikelihood = {res}-1478.062                {txt}Prob > chi2       =     {res}0.0000

{txt}{ralign 85:(Std. Err. adjusted for {res:147} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}punish              {txt}{c |}
{space 10}gwf_party {c |}{col 21}{res}{space 2}  -.67812{col 33}{space 2} .4150841{col 44}{space 1}   -1.63{col 53}{space 3}0.102{col 61}{space 4} -1.49167{col 74}{space 3} .1354299
{txt}{space 7}gwf_military {c |}{col 21}{res}{space 2}-1.063316{col 33}{space 2} .4002377{col 44}{space 1}   -2.66{col 53}{space 3}0.008{col 61}{space 4}-1.847768{col 74}{space 3}-.2788646
{txt}{space 8}gwf_monarch {c |}{col 21}{res}{space 2}-.5403309{col 33}{space 2} .6512449{col 44}{space 1}   -0.83{col 53}{space 3}0.407{col 61}{space 4}-1.816747{col 74}{space 3} .7360857
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2} -1.56379{col 33}{space 2} .4169787{col 44}{space 1}   -3.75{col 53}{space 3}0.000{col 61}{space 4}-2.381053{col 74}{space 3}-.7465266
{txt}{space 7}both_max_pts {c |}{col 21}{res}{space 2} .2029255{col 33}{space 2}  .090891{col 44}{space 1}    2.23{col 53}{space 3}0.026{col 61}{space 4} .0247823{col 74}{space 3} .3810686
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0428958{col 33}{space 2} .0119457{col 44}{space 1}    3.59{col 53}{space 3}0.000{col 61}{space 4} .0194827{col 74}{space 3}  .066309
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.3759009{col 33}{space 2} .2695741{col 44}{space 1}   -1.39{col 53}{space 3}0.163{col 61}{space 4}-.9042564{col 74}{space 3} .1524546
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2}-.0183319{col 33}{space 2} .2783176{col 44}{space 1}   -0.07{col 53}{space 3}0.947{col 61}{space 4}-.5638244{col 74}{space 3} .5271606
{txt}{space 11}irr_exit {c |}{col 21}{res}{space 2} 1.821128{col 33}{space 2} .5543195{col 44}{space 1}    3.29{col 53}{space 3}0.001{col 61}{space 4} .7346813{col 74}{space 3} 2.907574
{txt}{space 14}_cons {c |}{col 21}{res}{space 2} .3074534{col 33}{space 2} 1.495972{col 44}{space 1}    0.21{col 53}{space 3}0.837{col 61}{space 4}-2.624597{col 74}{space 3} 3.239504
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}fail                {txt}{c |}
{space 10}gwf_party {c |}{col 21}{res}{space 2} .1630555{col 33}{space 2} .1139368{col 44}{space 1}    1.43{col 53}{space 3}0.152{col 61}{space 4}-.0602565{col 74}{space 3} .3863676
{txt}{space 7}gwf_military {c |}{col 21}{res}{space 2} .6477548{col 33}{space 2} .1305369{col 44}{space 1}    4.96{col 53}{space 3}0.000{col 61}{space 4} .3919072{col 74}{space 3} .9036023
{txt}{space 8}gwf_monarch {c |}{col 21}{res}{space 2}-.2568874{col 33}{space 2}  .203361{col 44}{space 1}   -1.26{col 53}{space 3}0.207{col 61}{space 4}-.6554676{col 74}{space 3} .1416928
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2} 1.053101{col 33}{space 2} .1090891{col 44}{space 1}    9.65{col 53}{space 3}0.000{col 61}{space 4} .8392899{col 74}{space 3} 1.266911
{txt}{space 16}mid {c |}{col 21}{res}{space 2} -.005593{col 33}{space 2} .0552241{col 44}{space 1}   -0.10{col 53}{space 3}0.919{col 61}{space 4}-.1138302{col 74}{space 3} .1026442
{txt}{space 12}civ_war {c |}{col 21}{res}{space 2}  .307818{col 33}{space 2} .0864491{col 44}{space 1}    3.56{col 53}{space 3}0.000{col 61}{space 4}  .138381{col 74}{space 3} .4772551
{txt}{space 12}log_pop {c |}{col 21}{res}{space 2} .0095692{col 33}{space 2} .0210269{col 44}{space 1}    0.46{col 53}{space 3}0.649{col 61}{space 4}-.0316427{col 74}{space 3} .0507811
{txt}{space 10}log_gdppc {c |}{col 21}{res}{space 2} .1459756{col 33}{space 2} .0182562{col 44}{space 1}    8.00{col 53}{space 3}0.000{col 61}{space 4}  .110194{col 74}{space 3} .1817571
{txt}{space 10}gdpgrowth {c |}{col 21}{res}{space 2}-.1670388{col 33}{space 2} .2048047{col 44}{space 1}   -0.82{col 53}{space 3}0.415{col 61}{space 4}-.5684486{col 74}{space 3} .2343711
{txt}{space 2}prevtimesinoffice {c |}{col 21}{res}{space 2}-.0986614{col 33}{space 2} .0699161{col 44}{space 1}   -1.41{col 53}{space 3}0.158{col 61}{space 4}-.2356945{col 74}{space 3} .0383717
{txt}{space 16}age {c |}{col 21}{res}{space 2} .0016553{col 33}{space 2} .0025843{col 44}{space 1}    0.64{col 53}{space 3}0.522{col 61}{space 4}-.0034098{col 74}{space 3} .0067203
{txt}{space 10}failyears {c |}{col 21}{res}{space 2} .0660215{col 33}{space 2} .0253532{col 44}{space 1}    2.60{col 53}{space 3}0.009{col 61}{space 4} .0163302{col 74}{space 3} .1157128
{txt}{space 16}fy2 {c |}{col 21}{res}{space 2} -.002687{col 33}{space 2} .0017925{col 44}{space 1}   -1.50{col 53}{space 3}0.134{col 61}{space 4}-.0062001{col 74}{space 3} .0008262
{txt}{space 16}fy3 {c |}{col 21}{res}{space 2} .0000345{col 33}{space 2}  .000032{col 44}{space 1}    1.08{col 53}{space 3}0.281{col 61}{space 4}-.0000282{col 74}{space 3} .0000972
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-3.584486{col 33}{space 2} .3175472{col 44}{space 1}  -11.29{col 53}{space 3}0.000{col 61}{space 4}-4.206867{col 74}{space 3}-2.962105
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
            /athrho {c |}{col 21}{res}{space 2}-.6206012{col 33}{space 2} .6511964{col 44}{space 1}   -0.95{col 53}{space 3}0.341{col 61}{space 4}-1.896923{col 74}{space 3} .6557203
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                rho{col 21}{c |}{res}{space 2}-.5515465{col 33}{space 2} .4531002{col 61}{space 4}-.9559732{col 74}{space 3} .5755083
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Wald test of indep. eqns. (rho = 0): chi2({res}1{txt}) = {res}    0.91   {txt}Prob > chi2 = {res}0.3406
{txt}
{com}. estimates store a66
{txt}
{com}. 
. heckprobit punish gwf_party_wks gwf_military_wks gwf_monarch_wks gwf_democracy_wks  both_max_pts previous_sum_punish instit_control irr_entry irr_exit, sel(fail=gwf_party_wks gwf_military_wks gwf_monarch_wks gwf_democracy_wks mid civ_war log_pop log_gdppc gdpgrowth prevtimesinoffice age failyears fy2 fy3) cluster(ccode)

{txt}Fitting probit model:

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-195.87243}  
Iteration 1:{space 3}log pseudolikelihood = {res: -87.91138}  
Iteration 2:{space 3}log pseudolikelihood = {res:-84.968716}  
Iteration 3:{space 3}log pseudolikelihood = {res:-84.916261}  
Iteration 4:{space 3}log pseudolikelihood = {res:-84.916207}  
Iteration 5:{space 3}log pseudolikelihood = {res:-84.916207}  
{res}
{txt}Fitting selection model:

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1510.3899}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1353.5896}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1344.1242}  
Iteration 3:{space 3}log pseudolikelihood = {res: -1344.074}  
Iteration 4:{space 3}log pseudolikelihood = {res: -1344.074}  
{res}
{txt}Fitting starting values:

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-298.05329}  
Iteration 1:{space 3}log pseudolikelihood = {res:-90.720106}  
Iteration 2:{space 3}log pseudolikelihood = {res:-83.847811}  
Iteration 3:{space 3}log pseudolikelihood = {res: -83.70302}  
Iteration 4:{space 3}log pseudolikelihood = {res:-83.702821}  
Iteration 5:{space 3}log pseudolikelihood = {res:-83.702821}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1451.8437}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-1433.6156}  (not concave)
Iteration 2:{space 3}log pseudolikelihood = {res:-1430.9846}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1427.9951}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1427.8207}  
Iteration 5:{space 3}log pseudolikelihood = {res:-1427.7794}  
Iteration 6:{space 3}log pseudolikelihood = {res:-1427.7773}  
Iteration 7:{space 3}log pseudolikelihood = {res:-1427.7773}  
{res}
{txt}Probit model with sample selection              Number of obs     = {res}     5,521
{txt}                                                Censored obs      = {res}     5,091
                                                {txt}Uncensored obs    = {res}       430

                                                {txt}Wald chi2({res}9{txt})      =  {res}    72.97
{txt}Log pseudolikelihood = {res}-1427.777                {txt}Prob > chi2       =     {res}0.0000

{txt}{ralign 85:(Std. Err. adjusted for {res:144} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}punish              {txt}{c |}
{space 6}gwf_party_wks {c |}{col 21}{res}{space 2}-1.271809{col 33}{space 2} .4287135{col 44}{space 1}   -2.97{col 53}{space 3}0.003{col 61}{space 4}-2.112072{col 74}{space 3}-.4315461
{txt}{space 3}gwf_military_wks {c |}{col 21}{res}{space 2}-1.244873{col 33}{space 2} .3703113{col 44}{space 1}   -3.36{col 53}{space 3}0.001{col 61}{space 4} -1.97067{col 74}{space 3}-.5190765
{txt}{space 4}gwf_monarch_wks {c |}{col 21}{res}{space 2}-.8723053{col 33}{space 2} .6552461{col 44}{space 1}   -1.33{col 53}{space 3}0.183{col 61}{space 4}-2.156564{col 74}{space 3} .4119535
{txt}{space 2}gwf_democracy_wks {c |}{col 21}{res}{space 2}-1.833331{col 33}{space 2} .3784553{col 44}{space 1}   -4.84{col 53}{space 3}0.000{col 61}{space 4} -2.57509{col 74}{space 3}-1.091572
{txt}{space 7}both_max_pts {c |}{col 21}{res}{space 2} .1725584{col 33}{space 2} .0705015{col 44}{space 1}    2.45{col 53}{space 3}0.014{col 61}{space 4} .0343779{col 74}{space 3} .3107389
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0425614{col 33}{space 2} .0101815{col 44}{space 1}    4.18{col 53}{space 3}0.000{col 61}{space 4}  .022606{col 74}{space 3} .0625168
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.5176161{col 33}{space 2} .2904229{col 44}{space 1}   -1.78{col 53}{space 3}0.075{col 61}{space 4}-1.086835{col 74}{space 3} .0516023
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2} -.267253{col 33}{space 2}  .303138{col 44}{space 1}   -0.88{col 53}{space 3}0.378{col 61}{space 4}-.8613925{col 74}{space 3} .3268865
{txt}{space 11}irr_exit {c |}{col 21}{res}{space 2} 1.719084{col 33}{space 2}  .410341{col 44}{space 1}    4.19{col 53}{space 3}0.000{col 61}{space 4} .9148309{col 74}{space 3} 2.523338
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}  1.07115{col 33}{space 2} .7231251{col 44}{space 1}    1.48{col 53}{space 3}0.139{col 61}{space 4}-.3461492{col 74}{space 3} 2.488449
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}fail                {txt}{c |}
{space 6}gwf_party_wks {c |}{col 21}{res}{space 2}  .367884{col 33}{space 2} .0959926{col 44}{space 1}    3.83{col 53}{space 3}0.000{col 61}{space 4}  .179742{col 74}{space 3} .5560261
{txt}{space 3}gwf_military_wks {c |}{col 21}{res}{space 2} .8644175{col 33}{space 2} .1074368{col 44}{space 1}    8.05{col 53}{space 3}0.000{col 61}{space 4} .6538452{col 74}{space 3}  1.07499
{txt}{space 4}gwf_monarch_wks {c |}{col 21}{res}{space 2}-.2012595{col 33}{space 2} .1976311{col 44}{space 1}   -1.02{col 53}{space 3}0.309{col 61}{space 4}-.5886092{col 74}{space 3} .1860903
{txt}{space 2}gwf_democracy_wks {c |}{col 21}{res}{space 2} 1.132322{col 33}{space 2} .0965925{col 44}{space 1}   11.72{col 53}{space 3}0.000{col 61}{space 4} .9430038{col 74}{space 3} 1.321639
{txt}{space 16}mid {c |}{col 21}{res}{space 2} -.001454{col 33}{space 2} .0556583{col 44}{space 1}   -0.03{col 53}{space 3}0.979{col 61}{space 4}-.1105423{col 74}{space 3} .1076343
{txt}{space 12}civ_war {c |}{col 21}{res}{space 2} .3308987{col 33}{space 2} .0869875{col 44}{space 1}    3.80{col 53}{space 3}0.000{col 61}{space 4} .1604063{col 74}{space 3} .5013911
{txt}{space 12}log_pop {c |}{col 21}{res}{space 2} .0067647{col 33}{space 2} .0203958{col 44}{space 1}    0.33{col 53}{space 3}0.740{col 61}{space 4}-.0332104{col 74}{space 3} .0467397
{txt}{space 10}log_gdppc {c |}{col 21}{res}{space 2} .1482659{col 33}{space 2} .0175198{col 44}{space 1}    8.46{col 53}{space 3}0.000{col 61}{space 4} .1139276{col 74}{space 3} .1826041
{txt}{space 10}gdpgrowth {c |}{col 21}{res}{space 2}-.2252051{col 33}{space 2} .2054578{col 44}{space 1}   -1.10{col 53}{space 3}0.273{col 61}{space 4} -.627895{col 74}{space 3} .1774848
{txt}{space 2}prevtimesinoffice {c |}{col 21}{res}{space 2}-.0932819{col 33}{space 2} .0701841{col 44}{space 1}   -1.33{col 53}{space 3}0.184{col 61}{space 4}-.2308402{col 74}{space 3} .0442764
{txt}{space 16}age {c |}{col 21}{res}{space 2} .0013535{col 33}{space 2}  .002671{col 44}{space 1}    0.51{col 53}{space 3}0.612{col 61}{space 4}-.0038816{col 74}{space 3} .0065885
{txt}{space 10}failyears {c |}{col 21}{res}{space 2} .0656148{col 33}{space 2} .0248262{col 44}{space 1}    2.64{col 53}{space 3}0.008{col 61}{space 4} .0169564{col 74}{space 3} .1142732
{txt}{space 16}fy2 {c |}{col 21}{res}{space 2}-.0025165{col 33}{space 2} .0017326{col 44}{space 1}   -1.45{col 53}{space 3}0.146{col 61}{space 4}-.0059123{col 74}{space 3} .0008793
{txt}{space 16}fy3 {c |}{col 21}{res}{space 2} .0000323{col 33}{space 2} .0000314{col 44}{space 1}    1.03{col 53}{space 3}0.305{col 61}{space 4}-.0000293{col 74}{space 3} .0000939
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-3.643749{col 33}{space 2} .2900699{col 44}{space 1}  -12.56{col 53}{space 3}0.000{col 61}{space 4}-4.212275{col 74}{space 3}-3.075222
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
            /athrho {c |}{col 21}{res}{space 2}-.7949653{col 33}{space 2} .4020584{col 44}{space 1}   -1.98{col 53}{space 3}0.048{col 61}{space 4}-1.582985{col 74}{space 3}-.0069453
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                rho{col 21}{c |}{res}{space 2}-.6612126{col 33}{space 2} .2262776{col 61}{space 4}-.9190668{col 74}{space 3}-.0069451
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Wald test of indep. eqns. (rho = 0): chi2({res}1{txt}) = {res}    3.91   {txt}Prob > chi2 = {res}0.0480
{txt}
{com}. estimates store a67
{txt}
{com}. 
{txt}end of do-file

{com}. do "C:\Users\mtr012\AppData\Local\Temp\STD00000000.tmp"
{txt}
{com}. *Table A26
. 
. heckprobit punish wks_personal_scale gwf_democracy purges previous_sum_punish instit_control irr_entry, sel(fail=wks_personal_scale gwf_democracy mid civ_war log_pop log_gdppc gdpgrowth prevtimesinoffice age failyears fy2 fy3) cluster(ccode)

{txt}Fitting probit model:

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-264.48419}  
Iteration 1:{space 3}log pseudolikelihood = {res:-167.74499}  
Iteration 2:{space 3}log pseudolikelihood = {res:-165.29022}  
Iteration 3:{space 3}log pseudolikelihood = {res:-165.28799}  
Iteration 4:{space 3}log pseudolikelihood = {res:-165.28799}  
{res}
{txt}Fitting selection model:

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1690.1394}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1555.8817}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1552.7969}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1552.7871}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1552.7871}  
{res}
{txt}Fitting starting values:

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-399.94592}  
Iteration 1:{space 3}log pseudolikelihood = {res:-166.83281}  
Iteration 2:{space 3}log pseudolikelihood = {res:-163.51381}  
Iteration 3:{space 3}log pseudolikelihood = {res: -163.4962}  
Iteration 4:{space 3}log pseudolikelihood = {res: -163.4962}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1762.0787}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1720.6244}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1717.8662}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1716.9819}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1716.9445}  
Iteration 5:{space 3}log pseudolikelihood = {res:-1716.7625}  
Iteration 6:{space 3}log pseudolikelihood = {res:-1716.7621}  
Iteration 7:{space 3}log pseudolikelihood = {res:-1716.7621}  
{res}
{txt}Probit model with sample selection              Number of obs     = {res}     4,264
{txt}                                                Censored obs      = {res}     3,687
                                                {txt}Uncensored obs    = {res}       577

                                                {txt}Wald chi2({res}6{txt})      =  {res}    32.36
{txt}Log pseudolikelihood = {res}-1716.762                {txt}Prob > chi2       =     {res}0.0000

{txt}{ralign 85:(Std. Err. adjusted for {res:127} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}punish              {txt}{c |}
{space 1}wks_personal_scale {c |}{col 21}{res}{space 2} 1.531644{col 33}{space 2} .3714268{col 44}{space 1}    4.12{col 53}{space 3}0.000{col 61}{space 4} .8036605{col 74}{space 3} 2.259627
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2}-.2261014{col 33}{space 2} .2254475{col 44}{space 1}   -1.00{col 53}{space 3}0.316{col 61}{space 4}-.6679704{col 74}{space 3} .2157675
{txt}{space 13}purges {c |}{col 21}{res}{space 2} .2876901{col 33}{space 2} .0911235{col 44}{space 1}    3.16{col 53}{space 3}0.002{col 61}{space 4} .1090913{col 74}{space 3} .4662889
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0438511{col 33}{space 2} .0082892{col 44}{space 1}    5.29{col 53}{space 3}0.000{col 61}{space 4} .0276045{col 74}{space 3} .0600978
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.1844532{col 33}{space 2} .2352539{col 44}{space 1}   -0.78{col 53}{space 3}0.433{col 61}{space 4}-.6455423{col 74}{space 3} .2766359
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2}-.0170326{col 33}{space 2} .2028841{col 44}{space 1}   -0.08{col 53}{space 3}0.933{col 61}{space 4}-.4146781{col 74}{space 3} .3806129
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-2.057516{col 33}{space 2} .3447094{col 44}{space 1}   -5.97{col 53}{space 3}0.000{col 61}{space 4}-2.733134{col 74}{space 3}-1.381898
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}fail                {txt}{c |}
{space 1}wks_personal_scale {c |}{col 21}{res}{space 2}-.4420091{col 33}{space 2} .1080257{col 44}{space 1}   -4.09{col 53}{space 3}0.000{col 61}{space 4}-.6537356{col 74}{space 3}-.2302825
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2} .6394804{col 33}{space 2} .0795599{col 44}{space 1}    8.04{col 53}{space 3}0.000{col 61}{space 4} .4835458{col 74}{space 3} .7954149
{txt}{space 16}mid {c |}{col 21}{res}{space 2}-.0143687{col 33}{space 2} .0528116{col 44}{space 1}   -0.27{col 53}{space 3}0.786{col 61}{space 4}-.1178777{col 74}{space 3} .0891402
{txt}{space 12}civ_war {c |}{col 21}{res}{space 2} .0608417{col 33}{space 2} .0800111{col 44}{space 1}    0.76{col 53}{space 3}0.447{col 61}{space 4}-.0959772{col 74}{space 3} .2176606
{txt}{space 12}log_pop {c |}{col 21}{res}{space 2} .0046446{col 33}{space 2} .0172562{col 44}{space 1}    0.27{col 53}{space 3}0.788{col 61}{space 4}-.0291769{col 74}{space 3} .0384662
{txt}{space 10}log_gdppc {c |}{col 21}{res}{space 2}-.0520812{col 33}{space 2}   .02298{col 44}{space 1}   -2.27{col 53}{space 3}0.023{col 61}{space 4}-.0971212{col 74}{space 3}-.0070413
{txt}{space 10}gdpgrowth {c |}{col 21}{res}{space 2}-.0132332{col 33}{space 2} .1958597{col 44}{space 1}   -0.07{col 53}{space 3}0.946{col 61}{space 4}-.3971111{col 74}{space 3} .3706448
{txt}{space 2}prevtimesinoffice {c |}{col 21}{res}{space 2}-.0638507{col 33}{space 2} .0606316{col 44}{space 1}   -1.05{col 53}{space 3}0.292{col 61}{space 4}-.1826864{col 74}{space 3} .0549851
{txt}{space 16}age {c |}{col 21}{res}{space 2} .0032883{col 33}{space 2} .0020899{col 44}{space 1}    1.57{col 53}{space 3}0.116{col 61}{space 4}-.0008078{col 74}{space 3} .0073844
{txt}{space 10}failyears {c |}{col 21}{res}{space 2} .0903885{col 33}{space 2} .0247991{col 44}{space 1}    3.64{col 53}{space 3}0.000{col 61}{space 4} .0417833{col 74}{space 3} .1389938
{txt}{space 16}fy2 {c |}{col 21}{res}{space 2}-.0065353{col 33}{space 2} .0018053{col 44}{space 1}   -3.62{col 53}{space 3}0.000{col 61}{space 4}-.0100736{col 74}{space 3} -.002997
{txt}{space 16}fy3 {c |}{col 21}{res}{space 2} .0001282{col 33}{space 2} .0000353{col 44}{space 1}    3.63{col 53}{space 3}0.000{col 61}{space 4}  .000059{col 74}{space 3} .0001975
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-1.394507{col 33}{space 2}  .241788{col 44}{space 1}   -5.77{col 53}{space 3}0.000{col 61}{space 4}-1.868402{col 74}{space 3}-.9206109
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
            /athrho {c |}{col 21}{res}{space 2} .6543786{col 33}{space 2} .2875132{col 44}{space 1}    2.28{col 53}{space 3}0.023{col 61}{space 4}  .090863{col 74}{space 3} 1.217894
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                rho{col 21}{c |}{res}{space 2} .5746102{col 33}{space 2}  .192583{col 61}{space 4} .0906138{col 74}{space 3} .8390319
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Wald test of indep. eqns. (rho = 0): chi2({res}1{txt}) = {res}    5.18   {txt}Prob > chi2 = {res}0.0228
{txt}
{com}. estimates store a68
{txt}
{com}. 
. heckprobit punish wks_personal_scale gwf_democracy both_max_pts previous_sum_punish instit_control irr_entry, sel(fail= wks_personal_scale gwf_democracy mid civ_war log_pop log_gdppc gdpgrowth prevtimesinoffice age failyears fy2 fy3) cluster(ccode)

{txt}Fitting probit model:

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-148.50257}  
Iteration 1:{space 3}log pseudolikelihood = {res:-88.836559}  
Iteration 2:{space 3}log pseudolikelihood = {res:-87.872638}  
Iteration 3:{space 3}log pseudolikelihood = {res:-87.867841}  
Iteration 4:{space 3}log pseudolikelihood = {res: -87.86784}  
{res}
{txt}Fitting selection model:

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1138.2926}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1032.4926}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1026.7316}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1026.7066}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1026.7066}  
{res}
{txt}Fitting starting values:

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-228.04542}  
Iteration 1:{space 3}log pseudolikelihood = {res:-90.874489}  
Iteration 2:{space 3}log pseudolikelihood = {res:-87.812241}  
Iteration 3:{space 3}log pseudolikelihood = {res:-87.767153}  
Iteration 4:{space 3}log pseudolikelihood = {res:-87.767122}  
Iteration 5:{space 3}log pseudolikelihood = {res:-87.767122}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1114.5698}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1114.4827}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1114.4821}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1114.4821}  
{res}
{txt}Probit model with sample selection              Number of obs     = {res}     4,016
{txt}                                                Censored obs      = {res}     3,687
                                                {txt}Uncensored obs    = {res}       329

                                                {txt}Wald chi2({res}6{txt})      =  {res}    78.38
{txt}Log pseudolikelihood = {res}-1114.482                {txt}Prob > chi2       =     {res}0.0000

{txt}{ralign 85:(Std. Err. adjusted for {res:127} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}punish              {txt}{c |}
{space 1}wks_personal_scale {c |}{col 21}{res}{space 2} 2.452412{col 33}{space 2} .3699972{col 44}{space 1}    6.63{col 53}{space 3}0.000{col 61}{space 4} 1.727231{col 74}{space 3} 3.177593
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2}-.6482253{col 33}{space 2} .2393654{col 44}{space 1}   -2.71{col 53}{space 3}0.007{col 61}{space 4}-1.117373{col 74}{space 3}-.1790777
{txt}{space 7}both_max_pts {c |}{col 21}{res}{space 2} .2208499{col 33}{space 2} .0849237{col 44}{space 1}    2.60{col 53}{space 3}0.009{col 61}{space 4} .0544025{col 74}{space 3} .3872974
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0366094{col 33}{space 2} .0120003{col 44}{space 1}    3.05{col 53}{space 3}0.002{col 61}{space 4} .0130893{col 74}{space 3} .0601295
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.5542611{col 33}{space 2} .3674301{col 44}{space 1}   -1.51{col 53}{space 3}0.131{col 61}{space 4}-1.274411{col 74}{space 3} .1658887
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2} .0078178{col 33}{space 2} .3020521{col 44}{space 1}    0.03{col 53}{space 3}0.979{col 61}{space 4}-.5841935{col 74}{space 3} .5998291
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-.8830879{col 33}{space 2} 1.159214{col 44}{space 1}   -0.76{col 53}{space 3}0.446{col 61}{space 4}-3.155105{col 74}{space 3} 1.388929
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}fail                {txt}{c |}
{space 1}wks_personal_scale {c |}{col 21}{res}{space 2}-.7078209{col 33}{space 2} .1284509{col 44}{space 1}   -5.51{col 53}{space 3}0.000{col 61}{space 4}  -.95958{col 74}{space 3}-.4560618
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2} .5100313{col 33}{space 2} .0842137{col 44}{space 1}    6.06{col 53}{space 3}0.000{col 61}{space 4} .3449756{col 74}{space 3} .6750871
{txt}{space 16}mid {c |}{col 21}{res}{space 2}-.0110777{col 33}{space 2}  .064477{col 44}{space 1}   -0.17{col 53}{space 3}0.864{col 61}{space 4}-.1374503{col 74}{space 3} .1152949
{txt}{space 12}civ_war {c |}{col 21}{res}{space 2} .2841721{col 33}{space 2} .1077602{col 44}{space 1}    2.64{col 53}{space 3}0.008{col 61}{space 4} .0729661{col 74}{space 3} .4953781
{txt}{space 12}log_pop {c |}{col 21}{res}{space 2} .0040606{col 33}{space 2} .0239959{col 44}{space 1}    0.17{col 53}{space 3}0.866{col 61}{space 4}-.0429705{col 74}{space 3} .0510916
{txt}{space 10}log_gdppc {c |}{col 21}{res}{space 2} .1329583{col 33}{space 2} .0226492{col 44}{space 1}    5.87{col 53}{space 3}0.000{col 61}{space 4} .0885666{col 74}{space 3} .1773499
{txt}{space 10}gdpgrowth {c |}{col 21}{res}{space 2}-.1552772{col 33}{space 2} .2708991{col 44}{space 1}   -0.57{col 53}{space 3}0.567{col 61}{space 4}-.6862296{col 74}{space 3} .3756751
{txt}{space 2}prevtimesinoffice {c |}{col 21}{res}{space 2}-.1490658{col 33}{space 2}  .095618{col 44}{space 1}   -1.56{col 53}{space 3}0.119{col 61}{space 4}-.3364737{col 74}{space 3} .0383421
{txt}{space 16}age {c |}{col 21}{res}{space 2} .0027555{col 33}{space 2}  .002923{col 44}{space 1}    0.94{col 53}{space 3}0.346{col 61}{space 4}-.0029735{col 74}{space 3} .0084845
{txt}{space 10}failyears {c |}{col 21}{res}{space 2} .0789042{col 33}{space 2} .0286526{col 44}{space 1}    2.75{col 53}{space 3}0.006{col 61}{space 4} .0227463{col 74}{space 3} .1350622
{txt}{space 16}fy2 {c |}{col 21}{res}{space 2}-.0046806{col 33}{space 2} .0021238{col 44}{space 1}   -2.20{col 53}{space 3}0.028{col 61}{space 4}-.0088432{col 74}{space 3}-.0005179
{txt}{space 16}fy3 {c |}{col 21}{res}{space 2} .0000842{col 33}{space 2} .0000417{col 44}{space 1}    2.02{col 53}{space 3}0.044{col 61}{space 4} 2.42e-06{col 74}{space 3} .0001661
{txt}{space 14}_cons {c |}{col 21}{res}{space 2} -2.93463{col 33}{space 2} .3309364{col 44}{space 1}   -8.87{col 53}{space 3}0.000{col 61}{space 4}-3.583254{col 74}{space 3}-2.286007
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
            /athrho {c |}{col 21}{res}{space 2}  -.25073{col 33}{space 2} .5764026{col 44}{space 1}   -0.43{col 53}{space 3}0.664{col 61}{space 4}-1.380458{col 74}{space 3} .8789985
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                rho{col 21}{c |}{res}{space 2}-.2456047{col 33}{space 2} .5416331{col 61}{space 4}-.8810539{col 74}{space 3} .7059172
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Wald test of indep. eqns. (rho = 0): chi2({res}1{txt}) = {res}    0.19   {txt}Prob > chi2 = {res}0.6636
{txt}
{com}. estimates store a69
{txt}
{com}. 
. heckprobit punish pers_magaloni gwf_democracy both_max_pts previous_sum_punish instit_control irr_entry, sel(fail= pers_magaloni gwf_democracy mid civ_war log_pop log_gdppc gdpgrowth prevtimesinoffice age failyears fy2 fy3) cluster(ccode)

{txt}Fitting probit model:

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-204.20433}  
Iteration 1:{space 3}log pseudolikelihood = {res:-146.35878}  
Iteration 2:{space 3}log pseudolikelihood = {res: -144.0352}  
Iteration 3:{space 3}log pseudolikelihood = {res:-144.02319}  
Iteration 4:{space 3}log pseudolikelihood = {res:-144.02319}  
{res}
{txt}Fitting selection model:

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1524.7604}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1386.5757}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1381.6022}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1381.5873}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1381.5873}  
{res}
{txt}Fitting starting values:

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-300.13273}  
Iteration 1:{space 3}log pseudolikelihood = {res:-147.32291}  
Iteration 2:{space 3}log pseudolikelihood = {res:-143.77367}  
Iteration 3:{space 3}log pseudolikelihood = {res:-143.73639}  
Iteration 4:{space 3}log pseudolikelihood = {res:-143.73638}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1525.5967}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1525.2844}  
Iteration 2:{space 3}log pseudolikelihood = {res: -1525.275}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1525.2749}  
{res}
{txt}Probit model with sample selection              Number of obs     = {res}     5,607
{txt}                                                Censored obs      = {res}     5,174
                                                {txt}Uncensored obs    = {res}       433

                                                {txt}Wald chi2({res}6{txt})      =  {res}    99.08
{txt}Log pseudolikelihood = {res}-1525.275                {txt}Prob > chi2       =     {res}0.0000

{txt}{ralign 85:(Std. Err. adjusted for {res:147} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}punish              {txt}{c |}
{space 6}pers_magaloni {c |}{col 21}{res}{space 2} .4546188{col 33}{space 2} .2147446{col 44}{space 1}    2.12{col 53}{space 3}0.034{col 61}{space 4} .0337271{col 74}{space 3} .8755105
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2}-.5242231{col 33}{space 2} .3538352{col 44}{space 1}   -1.48{col 53}{space 3}0.138{col 61}{space 4}-1.217727{col 74}{space 3}  .169281
{txt}{space 7}both_max_pts {c |}{col 21}{res}{space 2} .3140525{col 33}{space 2} .0769614{col 44}{space 1}    4.08{col 53}{space 3}0.000{col 61}{space 4}  .163211{col 74}{space 3}  .464894
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0313116{col 33}{space 2} .0086484{col 44}{space 1}    3.62{col 53}{space 3}0.000{col 61}{space 4} .0143611{col 74}{space 3} .0482621
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.6363006{col 33}{space 2} .2711656{col 44}{space 1}   -2.35{col 53}{space 3}0.019{col 61}{space 4}-1.167775{col 74}{space 3}-.1048258
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2} .0456672{col 33}{space 2} .2388216{col 44}{space 1}    0.19{col 53}{space 3}0.848{col 61}{space 4}-.4224146{col 74}{space 3}  .513749
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-.7215248{col 33}{space 2} 1.276963{col 44}{space 1}   -0.57{col 53}{space 3}0.572{col 61}{space 4}-3.224327{col 74}{space 3} 1.781278
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}fail                {txt}{c |}
{space 6}pers_magaloni {c |}{col 21}{res}{space 2}-.0540505{col 33}{space 2} .0703983{col 44}{space 1}   -0.77{col 53}{space 3}0.443{col 61}{space 4}-.1920287{col 74}{space 3} .0839277
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2} .7533772{col 33}{space 2} .1088317{col 44}{space 1}    6.92{col 53}{space 3}0.000{col 61}{space 4} .5400709{col 74}{space 3} .9666835
{txt}{space 16}mid {c |}{col 21}{res}{space 2}-.0185897{col 33}{space 2} .0550694{col 44}{space 1}   -0.34{col 53}{space 3}0.736{col 61}{space 4}-.1265237{col 74}{space 3} .0893443
{txt}{space 12}civ_war {c |}{col 21}{res}{space 2} .3044239{col 33}{space 2} .0889454{col 44}{space 1}    3.42{col 53}{space 3}0.001{col 61}{space 4} .1300942{col 74}{space 3} .4787537
{txt}{space 12}log_pop {c |}{col 21}{res}{space 2} .0136261{col 33}{space 2} .0200141{col 44}{space 1}    0.68{col 53}{space 3}0.496{col 61}{space 4}-.0256008{col 74}{space 3} .0528531
{txt}{space 10}log_gdppc {c |}{col 21}{res}{space 2} .1455159{col 33}{space 2} .0171824{col 44}{space 1}    8.47{col 53}{space 3}0.000{col 61}{space 4}  .111839{col 74}{space 3} .1791927
{txt}{space 10}gdpgrowth {c |}{col 21}{res}{space 2}-.0503364{col 33}{space 2} .2305789{col 44}{space 1}   -0.22{col 53}{space 3}0.827{col 61}{space 4}-.5022627{col 74}{space 3} .4015899
{txt}{space 2}prevtimesinoffice {c |}{col 21}{res}{space 2}-.0989449{col 33}{space 2} .0722761{col 44}{space 1}   -1.37{col 53}{space 3}0.171{col 61}{space 4}-.2406034{col 74}{space 3} .0427136
{txt}{space 16}age {c |}{col 21}{res}{space 2} .0015808{col 33}{space 2} .0025987{col 44}{space 1}    0.61{col 53}{space 3}0.543{col 61}{space 4}-.0035125{col 74}{space 3} .0066742
{txt}{space 10}failyears {c |}{col 21}{res}{space 2} .0598448{col 33}{space 2} .0247393{col 44}{space 1}    2.42{col 53}{space 3}0.016{col 61}{space 4} .0113567{col 74}{space 3} .1083329
{txt}{space 16}fy2 {c |}{col 21}{res}{space 2} -.002774{col 33}{space 2} .0017925{col 44}{space 1}   -1.55{col 53}{space 3}0.122{col 61}{space 4}-.0062872{col 74}{space 3} .0007392
{txt}{space 16}fy3 {c |}{col 21}{res}{space 2} .0000373{col 33}{space 2} .0000323{col 44}{space 1}    1.15{col 53}{space 3}0.249{col 61}{space 4}-.0000261{col 74}{space 3} .0001006
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-3.296782{col 33}{space 2} .2756576{col 44}{space 1}  -11.96{col 53}{space 3}0.000{col 61}{space 4} -3.83706{col 74}{space 3}-2.756503
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
            /athrho {c |}{col 21}{res}{space 2}-.4078106{col 33}{space 2} .5712819{col 44}{space 1}   -0.71{col 53}{space 3}0.475{col 61}{space 4}-1.527502{col 74}{space 3} .7118813
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                rho{col 21}{c |}{res}{space 2}-.3866121{col 33}{space 2}  .485893{col 61}{space 4}-.9099962{col 74}{space 3} .6118552
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Wald test of indep. eqns. (rho = 0): chi2({res}1{txt}) = {res}    0.51   {txt}Prob > chi2 = {res}0.4753
{txt}
{com}. estimates store a70
{txt}
{com}. 
{txt}end of do-file

{com}. do "C:\Users\mtr012\AppData\Local\Temp\STD00000000.tmp"
{txt}
{com}. *Table A27
. 
. heckprobit punish wks_personal_scale wks_military_scale gwf_democracy  both_max_pts previous_sum_punish instit_control irr_entry, sel(fail= wks_personal_scale wks_military_scale gwf_democracy mid civ_war log_pop log_gdppc gdpgrowth prevtimesinoffice age failyears fy2 fy3) cluster(ccode)

{txt}Fitting probit model:

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -137.3206}  
Iteration 1:{space 3}log pseudolikelihood = {res: -79.55605}  
Iteration 2:{space 3}log pseudolikelihood = {res:-77.615905}  
Iteration 3:{space 3}log pseudolikelihood = {res:-77.600942}  
Iteration 4:{space 3}log pseudolikelihood = {res:-77.600937}  
{res}
{txt}Fitting selection model:

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1113.0846}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1001.7836}  
Iteration 2:{space 3}log pseudolikelihood = {res:-993.60926}  
Iteration 3:{space 3}log pseudolikelihood = {res:-993.54881}  
Iteration 4:{space 3}log pseudolikelihood = {res: -993.5488}  
{res}
{txt}Fitting starting values:

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-223.19339}  
Iteration 1:{space 3}log pseudolikelihood = {res:-80.022885}  
Iteration 2:{space 3}log pseudolikelihood = {res: -76.80872}  
Iteration 3:{space 3}log pseudolikelihood = {res:-76.764171}  
Iteration 4:{space 3}log pseudolikelihood = {res:-76.764164}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1083.1658}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-1072.4406}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1070.6359}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1070.4706}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1070.3593}  
Iteration 5:{space 3}log pseudolikelihood = {res:-1070.3527}  
Iteration 6:{space 3}log pseudolikelihood = {res:-1070.3523}  
Iteration 7:{space 3}log pseudolikelihood = {res:-1070.3523}  
{res}
{txt}Probit model with sample selection              Number of obs     = {res}     3,919
{txt}                                                Censored obs      = {res}     3,597
                                                {txt}Uncensored obs    = {res}       322

                                                {txt}Wald chi2({res}7{txt})      =  {res}    72.83
{txt}Log pseudolikelihood = {res}-1070.352                {txt}Prob > chi2       =     {res}0.0000

{txt}{ralign 85:(Std. Err. adjusted for {res:126} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}punish              {txt}{c |}
{space 1}wks_personal_scale {c |}{col 21}{res}{space 2} 1.925787{col 33}{space 2} .3953726{col 44}{space 1}    4.87{col 53}{space 3}0.000{col 61}{space 4} 1.150871{col 74}{space 3} 2.700704
{txt}{space 1}wks_military_scale {c |}{col 21}{res}{space 2}  1.12261{col 33}{space 2} .6023685{col 44}{space 1}    1.86{col 53}{space 3}0.062{col 61}{space 4}-.0580109{col 74}{space 3}  2.30323
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2}-.6664614{col 33}{space 2}  .223063{col 44}{space 1}   -2.99{col 53}{space 3}0.003{col 61}{space 4}-1.103657{col 74}{space 3} -.229266
{txt}{space 7}both_max_pts {c |}{col 21}{res}{space 2} .1073275{col 33}{space 2} .0808854{col 44}{space 1}    1.33{col 53}{space 3}0.185{col 61}{space 4}-.0512049{col 74}{space 3}   .26586
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0352759{col 33}{space 2}  .011548{col 44}{space 1}    3.05{col 53}{space 3}0.002{col 61}{space 4} .0126421{col 74}{space 3} .0579096
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.0653721{col 33}{space 2} .3112109{col 44}{space 1}   -0.21{col 53}{space 3}0.834{col 61}{space 4}-.6753342{col 74}{space 3}   .54459
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2} -.224774{col 33}{space 2} .3031768{col 44}{space 1}   -0.74{col 53}{space 3}0.458{col 61}{space 4}-.8189896{col 74}{space 3} .3694416
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-.3567127{col 33}{space 2} 1.088575{col 44}{space 1}   -0.33{col 53}{space 3}0.743{col 61}{space 4}-2.490281{col 74}{space 3} 1.776855
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}fail                {txt}{c |}
{space 1}wks_personal_scale {c |}{col 21}{res}{space 2}-1.008824{col 33}{space 2}  .140396{col 44}{space 1}   -7.19{col 53}{space 3}0.000{col 61}{space 4}-1.283995{col 74}{space 3}-.7336526
{txt}{space 1}wks_military_scale {c |}{col 21}{res}{space 2} .5156602{col 33}{space 2} .1216833{col 44}{space 1}    4.24{col 53}{space 3}0.000{col 61}{space 4} .2771653{col 74}{space 3} .7541552
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2} .6911405{col 33}{space 2} .0976523{col 44}{space 1}    7.08{col 53}{space 3}0.000{col 61}{space 4} .4997456{col 74}{space 3} .8825355
{txt}{space 16}mid {c |}{col 21}{res}{space 2}-.0091361{col 33}{space 2} .0634244{col 44}{space 1}   -0.14{col 53}{space 3}0.885{col 61}{space 4}-.1334456{col 74}{space 3} .1151733
{txt}{space 12}civ_war {c |}{col 21}{res}{space 2} .2789451{col 33}{space 2}   .10083{col 44}{space 1}    2.77{col 53}{space 3}0.006{col 61}{space 4} .0813218{col 74}{space 3} .4765684
{txt}{space 12}log_pop {c |}{col 21}{res}{space 2} .0050405{col 33}{space 2} .0224498{col 44}{space 1}    0.22{col 53}{space 3}0.822{col 61}{space 4}-.0389603{col 74}{space 3} .0490413
{txt}{space 10}log_gdppc {c |}{col 21}{res}{space 2} .1230591{col 33}{space 2} .0230987{col 44}{space 1}    5.33{col 53}{space 3}0.000{col 61}{space 4} .0777864{col 74}{space 3} .1683317
{txt}{space 10}gdpgrowth {c |}{col 21}{res}{space 2}-.2151991{col 33}{space 2} .2391369{col 44}{space 1}   -0.90{col 53}{space 3}0.368{col 61}{space 4}-.6838989{col 74}{space 3} .2535006
{txt}{space 2}prevtimesinoffice {c |}{col 21}{res}{space 2}-.1374429{col 33}{space 2} .0927553{col 44}{space 1}   -1.48{col 53}{space 3}0.138{col 61}{space 4}-.3192399{col 74}{space 3} .0443541
{txt}{space 16}age {c |}{col 21}{res}{space 2} .0031793{col 33}{space 2} .0029913{col 44}{space 1}    1.06{col 53}{space 3}0.288{col 61}{space 4}-.0026835{col 74}{space 3}  .009042
{txt}{space 10}failyears {c |}{col 21}{res}{space 2} .0788449{col 33}{space 2}  .028581{col 44}{space 1}    2.76{col 53}{space 3}0.006{col 61}{space 4} .0228272{col 74}{space 3} .1348627
{txt}{space 16}fy2 {c |}{col 21}{res}{space 2}-.0040492{col 33}{space 2} .0021386{col 44}{space 1}   -1.89{col 53}{space 3}0.058{col 61}{space 4}-.0082407{col 74}{space 3} .0001423
{txt}{space 16}fy3 {c |}{col 21}{res}{space 2} .0000737{col 33}{space 2} .0000419{col 44}{space 1}    1.76{col 53}{space 3}0.079{col 61}{space 4}-8.41e-06{col 74}{space 3} .0001558
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-3.097918{col 33}{space 2} .3184172{col 44}{space 1}   -9.73{col 53}{space 3}0.000{col 61}{space 4}-3.722005{col 74}{space 3}-2.473832
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
            /athrho {c |}{col 21}{res}{space 2}  -.72565{col 33}{space 2}  .517784{col 44}{space 1}   -1.40{col 53}{space 3}0.161{col 61}{space 4}-1.740488{col 74}{space 3}  .289188
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                rho{col 21}{c |}{res}{space 2}-.6203968{col 33}{space 2} .3184929{col 61}{space 4}-.9402832{col 74}{space 3} .2813872
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Wald test of indep. eqns. (rho = 0): chi2({res}1{txt}) = {res}    1.96   {txt}Prob > chi2 = {res}0.1611
{txt}
{com}. estimates store a71
{txt}
{com}. 
. heckprobit punish boss strongman machine gwf_democracy  purges previous_sum_punish instit_control irr_entry, sel(fail= boss strongman machine gwf_democracy mid civ_war log_pop log_gdppc gdpgrowth prevtimesinoffice age failyears fy2 fy3) cluster(ccode)

{txt}Fitting probit model:

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-244.22606}  
Iteration 1:{space 3}log pseudolikelihood = {res:-146.91788}  
Iteration 2:{space 3}log pseudolikelihood = {res:-143.80898}  
Iteration 3:{space 3}log pseudolikelihood = {res:-143.79372}  
Iteration 4:{space 3}log pseudolikelihood = {res:-143.79372}  
{res}
{txt}Fitting selection model:

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1651.0514}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1505.9409}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1501.5811}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1501.5667}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1501.5667}  
{res}
{txt}Fitting starting values:

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-390.93501}  
Iteration 1:{space 3}log pseudolikelihood = {res:-147.47167}  
Iteration 2:{space 3}log pseudolikelihood = {res:-143.51109}  
Iteration 3:{space 3}log pseudolikelihood = {res:-143.48755}  
Iteration 4:{space 3}log pseudolikelihood = {res:-143.48755}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1645.7955}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1645.1291}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1645.1136}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1645.1029}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1645.1027}  
Iteration 5:{space 3}log pseudolikelihood = {res:-1645.1027}  
{res}
{txt}Probit model with sample selection              Number of obs     = {res}     4,161
{txt}                                                Censored obs      = {res}     3,597
                                                {txt}Uncensored obs    = {res}       564

                                                {txt}Wald chi2({res}8{txt})      =  {res}   114.73
{txt}Log pseudolikelihood = {res}-1645.103                {txt}Prob > chi2       =     {res}0.0000

{txt}{ralign 85:(Std. Err. adjusted for {res:126} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}punish              {txt}{c |}
{space 15}boss {c |}{col 21}{res}{space 2}-.2849253{col 33}{space 2} .3930172{col 44}{space 1}   -0.72{col 53}{space 3}0.468{col 61}{space 4}-1.055225{col 74}{space 3} .4853742
{txt}{space 10}strongman {c |}{col 21}{res}{space 2} .6436433{col 33}{space 2} .3518882{col 44}{space 1}    1.83{col 53}{space 3}0.067{col 61}{space 4}-.0460448{col 74}{space 3} 1.333331
{txt}{space 12}machine {c |}{col 21}{res}{space 2}-1.515554{col 33}{space 2} .2077439{col 44}{space 1}   -7.30{col 53}{space 3}0.000{col 61}{space 4}-1.922725{col 74}{space 3}-1.108383
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2}-.3734529{col 33}{space 2} .4188603{col 44}{space 1}   -0.89{col 53}{space 3}0.373{col 61}{space 4}-1.194404{col 74}{space 3} .4474983
{txt}{space 13}purges {c |}{col 21}{res}{space 2} .1941543{col 33}{space 2} .0956838{col 44}{space 1}    2.03{col 53}{space 3}0.042{col 61}{space 4} .0066174{col 74}{space 3} .3816911
{txt}previous_sum_punish {c |}{col 21}{res}{space 2}  .040384{col 33}{space 2} .0091386{col 44}{space 1}    4.42{col 53}{space 3}0.000{col 61}{space 4} .0224727{col 74}{space 3} .0582952
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2} -.027585{col 33}{space 2}  .253763{col 44}{space 1}   -0.11{col 53}{space 3}0.913{col 61}{space 4}-.5249514{col 74}{space 3} .4697813
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2} -.376511{col 33}{space 2} .2517603{col 44}{space 1}   -1.50{col 53}{space 3}0.135{col 61}{space 4} -.869952{col 74}{space 3} .1169301
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-.5009987{col 33}{space 2} .6853596{col 44}{space 1}   -0.73{col 53}{space 3}0.465{col 61}{space 4}-1.844279{col 74}{space 3} .8422815
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}fail                {txt}{c |}
{space 15}boss {c |}{col 21}{res}{space 2}-.8836954{col 33}{space 2} .1361897{col 44}{space 1}   -6.49{col 53}{space 3}0.000{col 61}{space 4}-1.150622{col 74}{space 3}-.6167684
{txt}{space 10}strongman {c |}{col 21}{res}{space 2}-.5135539{col 33}{space 2} .1140517{col 44}{space 1}   -4.50{col 53}{space 3}0.000{col 61}{space 4} -.737091{col 74}{space 3}-.2900167
{txt}{space 12}machine {c |}{col 21}{res}{space 2}-.4418142{col 33}{space 2} .1103425{col 44}{space 1}   -4.00{col 53}{space 3}0.000{col 61}{space 4}-.6580816{col 74}{space 3}-.2255469
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2} .8676046{col 33}{space 2}  .092908{col 44}{space 1}    9.34{col 53}{space 3}0.000{col 61}{space 4} .6855083{col 74}{space 3} 1.049701
{txt}{space 16}mid {c |}{col 21}{res}{space 2}-.0043918{col 33}{space 2} .0550996{col 44}{space 1}   -0.08{col 53}{space 3}0.936{col 61}{space 4} -.112385{col 74}{space 3} .1036015
{txt}{space 12}civ_war {c |}{col 21}{res}{space 2} .0531827{col 33}{space 2}  .087336{col 44}{space 1}    0.61{col 53}{space 3}0.543{col 61}{space 4}-.1179928{col 74}{space 3} .2243581
{txt}{space 12}log_pop {c |}{col 21}{res}{space 2} .0045625{col 33}{space 2} .0175832{col 44}{space 1}    0.26{col 53}{space 3}0.795{col 61}{space 4}-.0298999{col 74}{space 3}  .039025
{txt}{space 10}log_gdppc {c |}{col 21}{res}{space 2}-.0555885{col 33}{space 2}  .023811{col 44}{space 1}   -2.33{col 53}{space 3}0.020{col 61}{space 4}-.1022572{col 74}{space 3}-.0089198
{txt}{space 10}gdpgrowth {c |}{col 21}{res}{space 2} -.053522{col 33}{space 2} .2102298{col 44}{space 1}   -0.25{col 53}{space 3}0.799{col 61}{space 4}-.4655647{col 74}{space 3} .3585208
{txt}{space 2}prevtimesinoffice {c |}{col 21}{res}{space 2}-.0510874{col 33}{space 2} .0619343{col 44}{space 1}   -0.82{col 53}{space 3}0.409{col 61}{space 4}-.1724764{col 74}{space 3} .0703017
{txt}{space 16}age {c |}{col 21}{res}{space 2} .0037802{col 33}{space 2} .0022413{col 44}{space 1}    1.69{col 53}{space 3}0.092{col 61}{space 4}-.0006127{col 74}{space 3} .0081731
{txt}{space 10}failyears {c |}{col 21}{res}{space 2} .0947865{col 33}{space 2} .0252214{col 44}{space 1}    3.76{col 53}{space 3}0.000{col 61}{space 4} .0453534{col 74}{space 3} .1442196
{txt}{space 16}fy2 {c |}{col 21}{res}{space 2} -.006462{col 33}{space 2} .0018102{col 44}{space 1}   -3.57{col 53}{space 3}0.000{col 61}{space 4}  -.01001{col 74}{space 3}-.0029141
{txt}{space 16}fy3 {c |}{col 21}{res}{space 2}  .000126{col 33}{space 2} .0000348{col 44}{space 1}    3.62{col 53}{space 3}0.000{col 61}{space 4} .0000578{col 74}{space 3} .0001942
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-1.210937{col 33}{space 2} .2446231{col 44}{space 1}   -4.95{col 53}{space 3}0.000{col 61}{space 4}-1.690389{col 74}{space 3}-.7314845
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
            /athrho {c |}{col 21}{res}{space 2} .3297318{col 33}{space 2} .4447358{col 44}{space 1}    0.74{col 53}{space 3}0.458{col 61}{space 4}-.5419344{col 74}{space 3} 1.201398
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                rho{col 21}{c |}{res}{space 2} .3182798{col 33}{space 2} .3996832{col 61}{space 4}-.4944508{col 74}{space 3} .8340805
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Wald test of indep. eqns. (rho = 0): chi2({res}1{txt}) = {res}    0.55   {txt}Prob > chi2 = {res}0.4584
{txt}
{com}. estimates store a72
{txt}
{com}. 
. heckprobit punish wks_personal_scale wks_military_scale gwf_democracy  both_max_pts previous_sum_punish instit_control irr_entry irr_exit, sel(fail= wks_personal_scale wks_military_scale gwf_democracy mid civ_war log_pop log_gdppc gdpgrowth prevtimesinoffice age failyears fy2 fy3) cluster(ccode)

{txt}Fitting probit model:

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res: -137.3206}  
Iteration 1:{space 3}log pseudolikelihood = {res:-65.639916}  
Iteration 2:{space 3}log pseudolikelihood = {res:-63.893321}  
Iteration 3:{space 3}log pseudolikelihood = {res: -63.86612}  
Iteration 4:{space 3}log pseudolikelihood = {res:-63.866088}  
Iteration 5:{space 3}log pseudolikelihood = {res:-63.866088}  
{res}
{txt}Fitting selection model:

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1113.0846}  
Iteration 1:{space 3}log pseudolikelihood = {res:-1001.7836}  
Iteration 2:{space 3}log pseudolikelihood = {res:-993.60926}  
Iteration 3:{space 3}log pseudolikelihood = {res:-993.54881}  
Iteration 4:{space 3}log pseudolikelihood = {res: -993.5488}  
{res}
{txt}Fitting starting values:

{res}{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-223.19339}  
Iteration 1:{space 3}log pseudolikelihood = {res:-67.635087}  
Iteration 2:{space 3}log pseudolikelihood = {res:-63.392146}  
Iteration 3:{space 3}log pseudolikelihood = {res: -63.32371}  
Iteration 4:{space 3}log pseudolikelihood = {res:-63.323692}  
Iteration 5:{space 3}log pseudolikelihood = {res:-63.323692}  
{res}
{txt}Fitting full model:
{res}
{txt}Iteration 0:{space 3}log pseudolikelihood = {res:-1062.7933}  (not concave)
Iteration 1:{space 3}log pseudolikelihood = {res:-1057.7471}  
Iteration 2:{space 3}log pseudolikelihood = {res:-1056.9541}  
Iteration 3:{space 3}log pseudolikelihood = {res:-1056.9076}  
Iteration 4:{space 3}log pseudolikelihood = {res:-1056.8959}  
Iteration 5:{space 3}log pseudolikelihood = {res:-1056.8958}  
Iteration 6:{space 3}log pseudolikelihood = {res:-1056.8958}  
{res}
{txt}Probit model with sample selection              Number of obs     = {res}     3,919
{txt}                                                Censored obs      = {res}     3,597
                                                {txt}Uncensored obs    = {res}       322

                                                {txt}Wald chi2({res}8{txt})      =  {res}    69.36
{txt}Log pseudolikelihood = {res}-1056.896                {txt}Prob > chi2       =     {res}0.0000

{txt}{ralign 85:(Std. Err. adjusted for {res:126} clusters in ccode)}
{hline 20}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 21}{c |}{col 33}    Robust
{col 21}{c |}      Coef.{col 33}   Std. Err.{col 45}      z{col 53}   P>|z|{col 61}     [95% Con{col 74}f. Interval]
{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}punish              {txt}{c |}
{space 1}wks_personal_scale {c |}{col 21}{res}{space 2} 1.545496{col 33}{space 2}  .480059{col 44}{space 1}    3.22{col 53}{space 3}0.001{col 61}{space 4} .6045978{col 74}{space 3} 2.486395
{txt}{space 1}wks_military_scale {c |}{col 21}{res}{space 2}  .070835{col 33}{space 2} .4633125{col 44}{space 1}    0.15{col 53}{space 3}0.878{col 61}{space 4}-.8372408{col 74}{space 3} .9789109
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2}-.6378477{col 33}{space 2} .2881836{col 44}{space 1}   -2.21{col 53}{space 3}0.027{col 61}{space 4}-1.202677{col 74}{space 3}-.0730181
{txt}{space 7}both_max_pts {c |}{col 21}{res}{space 2}   .12428{col 33}{space 2} .0853066{col 44}{space 1}    1.46{col 53}{space 3}0.145{col 61}{space 4} -.042918{col 74}{space 3} .2914779
{txt}previous_sum_punish {c |}{col 21}{res}{space 2} .0399659{col 33}{space 2} .0129729{col 44}{space 1}    3.08{col 53}{space 3}0.002{col 61}{space 4} .0145395{col 74}{space 3} .0653923
{txt}{space 5}instit_control {c |}{col 21}{res}{space 2}-.2688205{col 33}{space 2} .3217969{col 44}{space 1}   -0.84{col 53}{space 3}0.404{col 61}{space 4}-.8995308{col 74}{space 3} .3618898
{txt}{space 10}irr_entry {c |}{col 21}{res}{space 2}-.1271863{col 33}{space 2}  .321008{col 44}{space 1}   -0.40{col 53}{space 3}0.692{col 61}{space 4}-.7563504{col 74}{space 3} .5019779
{txt}{space 11}irr_exit {c |}{col 21}{res}{space 2} 1.547387{col 33}{space 2} .4910965{col 44}{space 1}    3.15{col 53}{space 3}0.002{col 61}{space 4} .5848552{col 74}{space 3} 2.509918
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-.5060336{col 33}{space 2} 1.276614{col 44}{space 1}   -0.40{col 53}{space 3}0.692{col 61}{space 4}-3.008151{col 74}{space 3} 1.996084
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}fail                {txt}{c |}
{space 1}wks_personal_scale {c |}{col 21}{res}{space 2}-1.012229{col 33}{space 2} .1411131{col 44}{space 1}   -7.17{col 53}{space 3}0.000{col 61}{space 4}-1.288805{col 74}{space 3} -.735652
{txt}{space 1}wks_military_scale {c |}{col 21}{res}{space 2} .5177604{col 33}{space 2} .1222816{col 44}{space 1}    4.23{col 53}{space 3}0.000{col 61}{space 4}  .278093{col 74}{space 3} .7574279
{txt}{space 6}gwf_democracy {c |}{col 21}{res}{space 2} .6927639{col 33}{space 2} .0980282{col 44}{space 1}    7.07{col 53}{space 3}0.000{col 61}{space 4} .5006321{col 74}{space 3} .8848957
{txt}{space 16}mid {c |}{col 21}{res}{space 2}-.0065007{col 33}{space 2} .0646055{col 44}{space 1}   -0.10{col 53}{space 3}0.920{col 61}{space 4}-.1331252{col 74}{space 3} .1201238
{txt}{space 12}civ_war {c |}{col 21}{res}{space 2} .2727817{col 33}{space 2}  .101807{col 44}{space 1}    2.68{col 53}{space 3}0.007{col 61}{space 4} .0732436{col 74}{space 3} .4723198
{txt}{space 12}log_pop {c |}{col 21}{res}{space 2}  .005503{col 33}{space 2} .0226266{col 44}{space 1}    0.24{col 53}{space 3}0.808{col 61}{space 4}-.0388444{col 74}{space 3} .0498503
{txt}{space 10}log_gdppc {c |}{col 21}{res}{space 2} .1225911{col 33}{space 2} .0234091{col 44}{space 1}    5.24{col 53}{space 3}0.000{col 61}{space 4} .0767101{col 74}{space 3}  .168472
{txt}{space 10}gdpgrowth {c |}{col 21}{res}{space 2} -.254529{col 33}{space 2}   .24335{col 44}{space 1}   -1.05{col 53}{space 3}0.296{col 61}{space 4}-.7314863{col 74}{space 3} .2224282
{txt}{space 2}prevtimesinoffice {c |}{col 21}{res}{space 2}-.1369041{col 33}{space 2} .0937815{col 44}{space 1}   -1.46{col 53}{space 3}0.144{col 61}{space 4}-.3207125{col 74}{space 3} .0469043
{txt}{space 16}age {c |}{col 21}{res}{space 2} .0028862{col 33}{space 2}  .003057{col 44}{space 1}    0.94{col 53}{space 3}0.345{col 61}{space 4}-.0031055{col 74}{space 3} .0088778
{txt}{space 10}failyears {c |}{col 21}{res}{space 2}  .079028{col 33}{space 2} .0289711{col 44}{space 1}    2.73{col 53}{space 3}0.006{col 61}{space 4} .0222457{col 74}{space 3} .1358103
{txt}{space 16}fy2 {c |}{col 21}{res}{space 2}-.0040416{col 33}{space 2} .0021679{col 44}{space 1}   -1.86{col 53}{space 3}0.062{col 61}{space 4}-.0082906{col 74}{space 3} .0002073
{txt}{space 16}fy3 {c |}{col 21}{res}{space 2} .0000736{col 33}{space 2} .0000422{col 44}{space 1}    1.74{col 53}{space 3}0.081{col 61}{space 4}-9.18e-06{col 74}{space 3} .0001563
{txt}{space 14}_cons {c |}{col 21}{res}{space 2}-3.081749{col 33}{space 2} .3284569{col 44}{space 1}   -9.38{col 53}{space 3}0.000{col 61}{space 4}-3.725513{col 74}{space 3}-2.437986
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
            /athrho {c |}{col 21}{res}{space 2}-.6272586{col 33}{space 2}  .576497{col 44}{space 1}   -1.09{col 53}{space 3}0.277{col 61}{space 4}-1.757172{col 74}{space 3} .5026547
{txt}{hline 20}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}                rho{col 21}{c |}{res}{space 2}-.5561616{col 33}{space 2} .3981774{col 61}{space 4}-.9421863{col 74}{space 3} .4642024
{txt}{hline 20}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
Wald test of indep. eqns. (rho = 0): chi2({res}1{txt}) = {res}    1.18   {txt}Prob > chi2 = {res}0.2766
{txt}
{com}. estimates store a73
{txt}
{com}. 
{txt}end of do-file

{com}. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}F:\Mussolini\FPA RnR v2\Appendix\Appendix Log.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}17 Sep 2018, 14:44:36
{txt}{.-}
{smcl}
{txt}{sf}{ul off}